do not support hypothesis

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What 'Fail to Reject' Means in a Hypothesis Test

Casarsa Guru/Getty Images

• Inferential Statistics
• Statistics Tutorials
• Probability & Games
• Descriptive Statistics
• Applications Of Statistics
• Math Tutorials
• Pre Algebra & Algebra
• Exponential Decay
• Worksheets By Grade
• Ph.D., Mathematics, Purdue University
• M.S., Mathematics, Purdue University
• B.A., Mathematics, Physics, and Chemistry, Anderson University

In statistics , scientists can perform a number of different significance tests to determine if there is a relationship between two phenomena. One of the first they usually perform is a null hypothesis test. In short, the null hypothesis states that there is no meaningful relationship between two measured phenomena. After a performing a test, scientists can:

• Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or
• Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)

Key Takeaways: The Null Hypothesis

• In a test of significance, the null hypothesis states that there is no meaningful relationship between two measured phenomena.

• By comparing the null hypothesis to an alternative hypothesis, scientists can either reject or fail to reject the null hypothesis.

• The null hypothesis cannot be positively proven. Rather, all that scientists can determine from a test of significance is that the evidence collected does or does not disprove the null hypothesis.

It is important to note that a failure to reject does not mean that the null hypothesis is true—only that the test did not prove it to be false. In some cases, depending on the experiment, a relationship may exist between two phenomena that is not identified by the experiment. In such cases, new experiments must be designed to rule out alternative hypotheses.

Null vs. Alternative Hypothesis

The null hypothesis is considered the default in a scientific experiment . In contrast, an alternative hypothesis is one that claims that there is a meaningful relationship between two phenomena. These two competing hypotheses can be compared by performing a statistical hypothesis test, which determines whether there is a statistically significant relationship between the data.

For example, scientists studying the water quality of a stream may wish to determine whether a certain chemical affects the acidity of the water. The null hypothesis—that the chemical has no effect on the water quality—can be tested by measuring the pH level of two water samples, one of which contains some of the chemical and one of which has been left untouched. If the sample with the added chemical is measurably more or less acidic—as determined through statistical analysis—it is a reason to reject the null hypothesis. If the sample's acidity is unchanged, it is a reason to not reject the null hypothesis.

When scientists design experiments, they attempt to find evidence for the alternative hypothesis. They do not try to prove that the null hypothesis is true. The null hypothesis is assumed to be an accurate statement until contrary evidence proves otherwise. As a result, a test of significance does not produce any evidence pertaining to the truth of the null hypothesis.

Failing to Reject vs. Accept

In an experiment, the null hypothesis and the alternative hypothesis should be carefully formulated such that one and only one of these statements is true. If the collected data supports the alternative hypothesis, then the null hypothesis can be rejected as false. However, if the data does not support the alternative hypothesis, this does not mean that the null hypothesis is true. All it means is that the null hypothesis has not been disproven—hence the term "failure to reject." A "failure to reject" a hypothesis should not be confused with acceptance.

In mathematics, negations are typically formed by simply placing the word “not” in the correct place. Using this convention, tests of significance allow scientists to either reject or not reject the null hypothesis. It sometimes takes a moment to realize that “not rejecting” is not the same as "accepting."

Null Hypothesis Example

In many ways, the philosophy behind a test of significance is similar to that of a trial. At the beginning of the proceedings, when the defendant enters a plea of “not guilty,” it is analogous to the statement of the null hypothesis. While the defendant may indeed be innocent, there is no plea of “innocent” to be formally made in court. The alternative hypothesis of “guilty” is what the prosecutor attempts to demonstrate.

The presumption at the outset of the trial is that the defendant is innocent. In theory, there is no need for the defendant to prove that he or she is innocent. The burden of proof is on the prosecuting attorney, who must marshal enough evidence to convince the jury that the defendant is guilty beyond a reasonable doubt. Likewise, in a test of significance, a scientist can only reject the null hypothesis by providing evidence for the alternative hypothesis.

If there is not enough evidence in a trial to demonstrate guilt, then the defendant is declared “not guilty.” This claim has nothing to do with innocence; it merely reflects the fact that the prosecution failed to provide enough evidence of guilt. In a similar way, a failure to reject the null hypothesis in a significance test does not mean that the null hypothesis is true. It only means that the scientist was unable to provide enough evidence for the alternative hypothesis.

For example, scientists testing the effects of a certain pesticide on crop yields might design an experiment in which some crops are left untreated and others are treated with varying amounts of pesticide. Any result in which the crop yields varied based on pesticide exposure—assuming all other variables are equal—would provide strong evidence for the alternative hypothesis (that the pesticide does affect crop yields). As a result, the scientists would have reason to reject the null hypothesis.

• Null Hypothesis Examples
• What Level of Alpha Determines Statistical Significance?
• How to Find Critical Values with a Chi-Square Table
• Example of a Chi-Square Goodness of Fit Test
• Hypothesis Test for the Difference of Two Population Proportions
• Type I and Type II Errors in Statistics
• Null Hypothesis and Alternative Hypothesis
• How to Conduct a Hypothesis Test
• What Is ANOVA?
• An Example of a Hypothesis Test
• What Is a P-Value?
• The Difference Between Type I and Type II Errors in Hypothesis Testing
• What Is a Hypothesis? (Science)
• What Is a Grand Jury and How Does It Work?
• Null Hypothesis Definition and Examples
• Hypothesis Test Example

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Top 7 Tried and Tested Paraphrasing Techniques

Annex vs Appendix: What is the difference?

• Privacy Policy

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

References in Research – Types, Examples and...

Research Paper Conclusion – Writing Guide and...

Research Topics – Ideas and Examples

Dissertation – Format, Example and Template

Conceptual Framework – Types, Methodology and...

Research Problem – Examples, Types and Guide

Support or Reject Null Hypothesis in Easy Steps

What does it mean to reject the null hypothesis.

• General Situations: P Value
• P Value Guidelines
• A Proportion
• A Proportion (second example)

In many statistical tests, you’ll want to either reject or support the null hypothesis . For elementary statistics students, the term can be a tricky term to grasp, partly because the name “null hypothesis” doesn’t make it clear about what the null hypothesis actually is!

The null hypothesis can be thought of as a nullifiable hypothesis. That means you can nullify it, or reject it. What happens if you reject the null hypothesis? It gets replaced with the alternate hypothesis, which is what you think might actually be true about a situation. For example, let’s say you think that a certain drug might be responsible for a spate of recent heart attacks. The drug company thinks the drug is safe. The null hypothesis is always the accepted hypothesis; in this example, the drug is on the market, people are using it, and it’s generally accepted to be safe. Therefore, the null hypothesis is that the drug is safe. The alternate hypothesis — the one you want to replace the null hypothesis, is that the drug isn’t safe. Rejecting the null hypothesis in this case means that you will have to prove that the drug is not safe.

To reject the null hypothesis, perform the following steps:

Step 1: State the null hypothesis. When you state the null hypothesis, you also have to state the alternate hypothesis. Sometimes it is easier to state the alternate hypothesis first, because that’s the researcher’s thoughts about the experiment. How to state the null hypothesis (opens in a new window).

Step 2: Support or reject the null hypothesis . Several methods exist, depending on what kind of sample data you have. For example, you can use the P-value method. For a rundown on all methods, see: Support or reject the null hypothesis.

If you are able to reject the null hypothesis in Step 2, you can replace it with the alternate hypothesis.

That’s it!

When to Reject the Null hypothesis

Basically, you reject the null hypothesis when your test value falls into the rejection region . There are four main ways you’ll compute test values and either support or reject your null hypothesis. Which method you choose depends mainly on if you have a proportion or a p-value .

Support or Reject the Null Hypothesis: Steps

Click the link the skip to the situation you need to support or reject null hypothesis for: General Situations: P Value P Value Guidelines A Proportion A Proportion (second example)

Support or Reject Null Hypothesis with a P Value

If you have a P-value , or are asked to find a p-value, follow these instructions to support or reject the null hypothesis. This method works if you are given an alpha level and if you are not given an alpha level. If you are given a confidence level , just subtract from 1 to get the alpha level. See: How to calculate an alpha level .

Step 1: State the null hypothesis and the alternate hypothesis (“the claim”). If you aren’t sure how to do this, follow this link for How To State the Null and Alternate Hypothesis .

Step 2: Find the critical value . We’re dealing with a normally distributed population, so the critical value is a z-score . Use the following formula to find the z-score .

Click here if you want easy, step-by-step instructions for solving this formula.

Step 4: Find the P-Value by looking up your answer from step 3 in the z-table . To get the p-value, subtract the area from 1. For example, if your area is .990 then your p-value is 1-.9950 = 0.005. Note: for a two-tailed test , you’ll need to halve this amount to get the p-value in one tail.

Step 5: Compare your answer from step 4 with the α value given in the question. Should you support or reject the null hypothesis? If step 7 is less than or equal to α, reject the null hypothesis, otherwise do not reject it.

P-Value Guidelines

Use these general guidelines to decide if you should reject or keep the null:

If p value > .10 → “not significant ” If p value ≤ .10 → “marginally significant” If p value ≤ .05 → “significant” If p value ≤ .01 → “highly significant.”

Back to Top

Support or Reject Null Hypothesis for a Proportion

Sometimes, you’ll be given a proportion of the population or a percentage and asked to support or reject null hypothesis. In this case you can’t compute a test value by calculating a z-score (you need actual numbers for that), so we use a slightly different technique.

Example question: A researcher claims that Democrats will win the next election. 4300 voters were polled; 2200 said they would vote Democrat. Decide if you should support or reject null hypothesis. Is there enough evidence at α=0.05 to support this claim?

Step 1: State the null hypothesis and the alternate hypothesis (“the claim”) . H o :p ≤ 0.5 H 1 :p > .5

Step 3: Use the following formula to calculate your test value.

Where: Phat is calculated in Step 2 P the null hypothesis p value (.05) Q is 1 – p

The z-score is: .512 – .5 / √(.5(.5) / 4300)) = 1.57

Step 4: Look up Step 3 in the z-table to get .9418.

Step 5: Calculate your p-value by subtracting Step 4 from 1. 1-.9418 = .0582

Step 6: Compare your answer from step 5 with the α value given in the question . Support or reject the null hypothesis? If step 5 is less than α, reject the null hypothesis, otherwise do not reject it. In this case, .582 (5.82%) is not less than our α, so we do not reject the null hypothesis.

Support or Reject Null Hypothesis for a Proportion: Second example

Example question: A researcher claims that more than 23% of community members go to church regularly. In a recent survey, 126 out of 420 people stated they went to church regularly. Is there enough evidence at α = 0.05 to support this claim? Use the P-Value method to support or reject null hypothesis.

Step 1: State the null hypothesis and the alternate hypothesis (“the claim”) . H o :p ≤ 0.23; H 1 :p > 0.23 (claim)

Step 3: Find ‘p’ by converting the stated claim to a decimal: 23% = 0.23. Also, find ‘q’ by subtracting ‘p’ from 1: 1 – 0.23 = 0.77.

Step 4: Use the following formula to calculate your test value.

If formulas confuse you, this is asking you to:

• Multiply p and q together, then divide by the number in the random sample. (0.23 x 0.77) / 420 = 0.00042
• Take the square root of your answer to 2 . √( 0.1771) = 0. 0205
• Divide your answer to 1. by your answer in 3. 0.07 / 0. 0205 = 3.41

Step 5: Find the P-Value by looking up your answer from step 5 in the z-table . The z-score for 3.41 is .4997. Subtract from 0.500: 0.500-.4997 = 0.003.

Step 6: Compare your P-value to α . Support or reject null hypothesis? If the P-value is less, reject the null hypothesis. If the P-value is more, keep the null hypothesis. 0.003 < 0.05, so we have enough evidence to reject the null hypothesis and accept the claim.

Note: In Step 5, I’m using the z-table on this site to solve this problem. Most textbooks have the right of z-table . If you’re seeing .9997 as an answer in your textbook table, then your textbook has a “whole z” table, in which case don’t subtract from .5, subtract from 1. 1-.9997 = 0.003.

Check out our Youtube channel for video tips!

Everitt, B. S.; Skrondal, A. (2010), The Cambridge Dictionary of Statistics , Cambridge University Press. Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial.

• Appointments
• Our Providers
• For Physicians
• When scientific hypotheses don’t pan out

One pair of scientists thought they’d discovered a new antiviral protein buried inside skin cells. Another research team saw early hints suggesting that the flu virus might cooperate to boost infections in humans. And a nationwide team of clinicians thought that high doses of certain vitamins might prevent cancer.

These studies don’t have much to do with each other, except that the researchers had all based their hypotheses on convincing earlier data.

And those hypotheses were all wrong.

The hypothesis is a central tenet to scientific research. Scientists ask questions, but a question on its own is often not sufficient to outline the experiments needed to answer it (nor to garner the funding needed to support those experiments).

So researchers construct a hypothesis, their best educated guess as to the answer to that question.

How a hypothesis is formed

Technically speaking, a hypothesis is only a hypothesis if it can be tested. Otherwise, it’s just an idea to discuss at the water cooler.

Researchers are always prepared for the possibility that those tests could disprove their hypotheses — that’s part of the reason they do the studies. But what happens when a beloved idea or dogma is shattered is less technical, less predictable. More human.

In some cases, a disproven hypothesis is devastating, said Swedish Cancer Institute and Fred Hutchinson Cancer Research Center public health researcher Dr. Gary Goodman, who led one of those vitamin studies. In his case, he was part of a group of cancer prevention researchers who ultimately showed that high doses of certain vitamins can increase the risk of lung cancer — an important result, but the opposite of what they thought they would prove in their trials.

But for some, finding a hypothesis to be false is exhilarating and motivating.

Herpes hypothesis leads to surprise cancer-related finding

Dr. Jia Zhu , a Fred Hutch infectious disease scientist, and her research partner (and husband), Fred Hutch and University of Washington infectious disease researcher Dr. Tao Peng, thought they’d found a new antiviral in herpes simplex virus type 2, or HSV-2, in part because they’ve been focused on that virus — and its interaction with human immune cells — for decades now, together with Dr. Larry Corey , virologist and president and director emeritus of Fred Hutch.

A few years ago, Zhu and Peng found that a tiny, mysterious protein called interleukin-17c is massively overproduced by HSV-infected skin cells. Maybe it was an undiscovered antiviral protein, the virologists thought, made by the skin cells in an attempt to protect themselves. They spent more than half a year pursuing that hypothesis, conducting experiment after experiment to see if IL-17c could block the herpes virus from replicating. It didn’t.

Zhu pointed to a microscopic image of a biopsy from a person with HSV, captured more than 10 years ago where she, Corey and their colleagues first discovered that certain T cells, a type of immune cell, cluster in the skin where herpes lesions form. At the top of the colorful image, a layer of skin cells stained blue is studded with orange-colored T cells. Beneath, green nerve endings stretch their branch-like fibers toward the infected skin cells.

“This is my favorite image, but we all focused on the top,” the skin and immune cells, Zhu said. “We never really paid attention to the nerves.”

"You take an approach and then you just have to let the science drive." — Dr. Jia Zhu, infectious disease researcher

Finally, Peng discovered that the nerve fibers themselves carry proteins that can interact with the IL-17c molecule produced in infected skin cells — and that the protein signals the nerves to grow, making it one of only a handful of nerve growth factors identified in humans.

The researchers are excited about their serendipitous finding not just because it’s another piece in the puzzle of this mysterious virus, which infects one in six teens and adults in the U.S. They also hope the protein could fuel new therapies in other settings — such as neuropathy, a type of nerve damage that is a side effect of many cancer chemotherapies.

It’s a finding they never would have anticipated, Zhu said, but that’s often the nature of research.

“You do have a big picture, you know the direction. You take an approach and then you just have to let the science drive,” she said. “If things are unexpected, maybe just explore a little bit more instead of shutting that door.”

Flu hypothesis leads to a new mindset and avenue of research

Sometimes, a mistaken hypothesis has less to do with researchers’ preconceptions and more to do with the way basic research is conducted. Take, for example, the work of Fred Hutch evolutionary biologist Dr. Jesse Bloom , whose laboratory team studies how influenza and other viruses evolve over time. Many of their experiments involve infecting human cells in a petri dish with different strains of the flu virus and seeing what happens.

A few years ago, Bloom and University of Washington doctoral student Katherine Xue made an intriguing discovery using that system: They saw that two variants of influenza H3N2 (the virus that’s wreaking havoc in the current flu season) could cooperate to infect cells better together than either version could alone.

The researchers had only shown that viral collaboration in petri dishes in the lab, but they had reason to think it might be happening in people, too. For one, the same mix of variants was present in public databases of samples taken from infected people — but those samples had also been grown in petri dishes in the lab before their genomic information was captured.

So Xue and Bloom sequenced those variants at their source, the original nasal wash samples collected and stored by the Washington State Public Health Laboratories . They found no such mixture of variants from the samples that hadn’t been grown in the laboratory — so the flu may not cooperate after all, at least not in our bodies. The researchers published their findings last month in the journal mSphere.

Scientists have to ask themselves two questions about any discovery, Bloom said: “Are your findings correct? And are they relevant?”

The team’s first study wasn’t wrong; the viruses do cooperate in cells in the lab. But the second question is usually the tougher one, the researchers said.

“There are a lot of differences, obviously, between viruses growing in a controlled setting in a petri dish versus an actual human,” Xue said.

She and Bloom aren’t too glum about their disproven hypothesis, though. That line of inquiry opened new doors in the lab, Bloom said.

Before Xue’s study, he and his colleagues exclusively studied viruses in petri dishes. Now, more members of his laboratory team are using clinical samples as well — an approach that is made possible by the closer collaborations between basic and clinical research at the Hutch, Bloom said.

Some of their findings in petri dishes aren’t holding true in the clinical samples. But they’re already making interesting findings about how flu evolves in the human body — including the discovery that how flu evolves in single people with unusually long infections can hint at how the virus will evolve globally, years later. They never would have done that study if they hadn’t already been trying to follow up their original, cooperating hypothesis.

“It opened this whole new way of trying to think about this,” Bloom said. “Our mindset has changed a lot.”

Prevention hypothesis flipped on its head

Fred Hutch and Swedish cancer prevention researcher Goodman and his epidemiology colleagues had good reason to think the vitamins they were testing in clinical trials could prevent lung cancer.

All of the data pointed to an association between the vitamins and a reduced risk of lung cancer. But the studies hadn’t shown a causative link — just a correlation. So the researchers set out to do large clinical trials comparing high doses of the vitamins to placebos.

In the CARET trial , which Goodman led and was initiated in 1985, 18,000 people at high risk of lung cancer (primarily smokers) were assigned to take either a placebo, vitamin A, beta-carotene (a vitamin A precursor) or a combination of the two supplements. Two other similar trials started in other parts of the world at around the same time also testing beta-carotene’s effect on lung cancer risk.

In a similar vein, at the same time, a small trial suggested that supplemental selenium decreased the incidence of prostate cancer. So in 2001, the SELECT trial launched through SWOG , a nationwide cancer clinical trial consortium, testing whether selenium or high-dose vitamin E or the combination could prevent prostate cancer. SELECT enrolled 35,000 men; Goodman was the study leader for the Seattle area.

Designing and conducting cancer prevention trials where participants take a drug or some other intervention is a tricky business, Goodman said.

“In prevention, most of the people you treat are healthy and will never get cancer,” he said. “So you have to make sure the agent is very safe.”

Previous studies had all pointed to the vitamins being safe — even beneficial. And the vitamins tested in the trials are all naturally occurring as part of our diets. Nobody thought they could possibly hurt.

But that’s exactly what happened. In the CARET study, participants taking the combination of vitamin A and beta-carotene had higher rates of lung cancer than those taking the placebo; other trials testing those vitamins saw similar results. And in the SELECT trial, those taking vitamin E had higher rates of prostate cancer.

All the trials had close monitoring built in and all were stopped early when the researchers saw that the cancer rates were trending the opposite way that they’d expected.

“It was just devastating when we learned the results,” Goodman said. “Everybody [who worked on the trial] was so hopeful. After all, we’re here to prevent cancer.”

When the CARET study stopped, Goodman and his team hired extra people to answer study participants’ questions and the angry phone calls they assumed they would get. But very few phone calls came in.

“They said they were involved in the study for altruistic reasons, and we got an answer,” he said. “One of the benefits of our study is that we did show that high doses of vitamins can be very harmful.”

That was an important finding, Goodman said, because the prevailing dogma at the time was that high doses of vitamins were good for you. Although these studies disproved that commonly held belief, even today not everyone in the general public buys that message.

Another benefit of that difficult experience: The bar for giving healthy people a supplement or drug with the goal of preventing cancer or other disease is much higher now, Goodman said.

“In prevention, [these studies] really changed people’s perceptions about what kind of evidence you need to have before you can invest the time, money, effort, human resources, people’s lives in an intervention study,” he said. “You really need to have good data suggesting that an intervention will be beneficial.”

rachel-tompa

Rachel Tompa is a former staff writer at Fred Hutchinson Cancer Center. She has a Ph.D. in molecular biology from the University of California, San Francisco and a certificate in science writing from the University of California, Santa Cruz. Follow her on Twitter @Rachel_Tompa .

Related News

Help us eliminate cancer.

Every dollar counts. Please support lifesaving research today.

• Jesse Bloom
• Gary E Goodman
• Lawrence Corey
• Vaccine and Infectious Disease
• Basic Sciences
• Public Health Sciences
• viral evolution
• epidemiology
• cancer prevention

For the Media

• Contact Media Relations
• News Releases
• Media Coverage
• About Fred Hutch
• Fred Hutchinson Cancer Center

How to Write Hypothesis Test Conclusions (With Examples)

A   hypothesis test is used to test whether or not some hypothesis about a population parameter is true.

To perform a hypothesis test in the real world, researchers obtain a random sample from the population and perform a hypothesis test on the sample data, using a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

If the p-value of the hypothesis test is less than some significance level (e.g. α = .05), then we reject the null hypothesis .

Otherwise, if the p-value is not less than some significance level then we fail to reject the null hypothesis .

When writing the conclusion of a hypothesis test, we typically include:

• Whether we reject or fail to reject the null hypothesis.
• The significance level.
• A short explanation in the context of the hypothesis test.

For example, we would write:

We reject the null hypothesis at the 5% significance level.   There is sufficient evidence to support the claim that…

Or, we would write:

We fail to reject the null hypothesis at the 5% significance level.   There is not sufficient evidence to support the claim that…

The following examples show how to write a hypothesis test conclusion in both scenarios.

Example 1: Reject the Null Hypothesis Conclusion

Suppose a biologist believes that a certain fertilizer will cause plants to grow more during a one-month period than they normally do, which is currently 20 inches. To test this, she applies the fertilizer to each of the plants in her laboratory for one month.

She then performs a hypothesis test at a 5% significance level using the following hypotheses:

• H 0 : μ = 20 inches (the fertilizer will have no effect on the mean plant growth)
• H A : μ > 20 inches (the fertilizer will cause mean plant growth to increase)

Suppose the p-value of the test turns out to be 0.002.

Here is how she would report the results of the hypothesis test:

We reject the null hypothesis at the 5% significance level.   There is sufficient evidence to support the claim that this particular fertilizer causes plants to grow more during a one-month period than they normally do.

Example 2: Fail to Reject the Null Hypothesis Conclusion

Suppose the manager of a manufacturing plant wants to test whether or not some new method changes the number of defective widgets produced per month, which is currently 250. To test this, he measures the mean number of defective widgets produced before and after using the new method for one month.

He performs a hypothesis test at a 10% significance level using the following hypotheses:

• H 0 : μ after = μ before (the mean number of defective widgets is the same before and after using the new method)
• H A : μ after ≠ μ before (the mean number of defective widgets produced is different before and after using the new method)

Suppose the p-value of the test turns out to be 0.27.

Here is how he would report the results of the hypothesis test:

We fail to reject the null hypothesis at the 10% significance level.   There is not sufficient evidence to support the claim that the new method leads to a change in the number of defective widgets produced per month.

Additional Resources

The following tutorials provide additional information about hypothesis testing:

Introduction to Hypothesis Testing 4 Examples of Hypothesis Testing in Real Life How to Write a Null Hypothesis

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It’s the default assumption unless empirical evidence proves otherwise.

The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).

The null hypothesis is the statement that a researcher or an investigator wants to disprove.

Testing the null hypothesis can tell you whether your results are due to the effects of manipulating ​ the dependent variable or due to random chance. 

How to Write a Null Hypothesis

Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables.

It is a default position that your research aims to challenge or confirm.

For example, if studying the impact of exercise on weight loss, your null hypothesis might be:

There is no significant difference in weight loss between individuals who exercise daily and those who do not.

Examples of Null Hypotheses

When Do We Reject The Null Hypothesis? 

We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.

If the collected data does not meet the expectation of the null hypothesis, a researcher can conclude that the data lacks sufficient evidence to back up the null hypothesis, and thus the null hypothesis is rejected. 

Rejecting the null hypothesis means that a relationship does exist between a set of variables and the effect is statistically significant ( p > 0.05).

If the data collected from the random sample is not statistically significance , then the null hypothesis will be accepted, and the researchers can conclude that there is no relationship between the variables. 

You need to perform a statistical test on your data in order to evaluate how consistent it is with the null hypothesis. A p-value is one statistical measurement used to validate a hypothesis against observed data.

Calculating the p-value is a critical part of null-hypothesis significance testing because it quantifies how strongly the sample data contradicts the null hypothesis.

The level of statistical significance is often expressed as a  p  -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01) as general guidelines to decide if you should reject or keep the null.

When your p-value is less than or equal to your significance level, you reject the null hypothesis.

In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.

In this case, the sample data provides insufficient data to conclude that the effect exists in the population.

Because you can never know with complete certainty whether there is an effect in the population, your inferences about a population will sometimes be incorrect.

When you incorrectly reject the null hypothesis, it’s called a type I error. When you incorrectly fail to reject it, it’s called a type II error.

Why Do We Never Accept The Null Hypothesis?

The reason we do not say “accept the null” is because we are always assuming the null hypothesis is true and then conducting a study to see if there is evidence against it. And, even if we don’t find evidence against it, a null hypothesis is not accepted.

A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. 

It is risky to conclude that the null hypothesis is true merely because we did not find evidence to reject it. It is always possible that researchers elsewhere have disproved the null hypothesis, so we cannot accept it as true, but instead, we state that we failed to reject the null. 

One can either reject the null hypothesis, or fail to reject it, but can never accept it.

Why Do We Use The Null Hypothesis?

We can never prove with 100% certainty that a hypothesis is true; We can only collect evidence that supports a theory. However, testing a hypothesis can set the stage for rejecting or accepting this hypothesis within a certain confidence level.

The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence).

A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis.

Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. 

Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.

It helps provide sufficient statistical evidence that either favors or rejects a certain hypothesis about the population parameter. 

Purpose of a Null Hypothesis 

• The primary purpose of the null hypothesis is to disprove an assumption. 
• Whether rejected or accepted, the null hypothesis can help further progress a theory in many scientific cases.
• A null hypothesis can be used to ascertain how consistent the outcomes of multiple studies are.

Do you always need both a Null Hypothesis and an Alternative Hypothesis?

The null (H0) and alternative (Ha or H1) hypotheses are two competing claims that describe the effect of the independent variable on the dependent variable. They are mutually exclusive, which means that only one of the two hypotheses can be true. 

While the null hypothesis states that there is no effect in the population, an alternative hypothesis states that there is statistical significance between two variables. 

The goal of hypothesis testing is to make inferences about a population based on a sample. In order to undertake hypothesis testing, you must express your research hypothesis as a null and alternative hypothesis. Both hypotheses are required to cover every possible outcome of the study. 

What is the difference between a null hypothesis and an alternative hypothesis?

The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.

It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.

What are some problems with the null hypothesis?

One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.

Why can a null hypothesis not be accepted?

We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.

We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.

Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.

If the p-value is greater than the significance level, then you fail to reject the null hypothesis.

Is a null hypothesis directional or non-directional?

A hypothesis test can either contain an alternative directional hypothesis or a non-directional alternative hypothesis. A directional hypothesis is one that contains the less than (“<“) or greater than (“>”) sign.

A nondirectional hypothesis contains the not equal sign (“≠”).  However, a null hypothesis is neither directional nor non-directional.

A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables.

The directional hypothesis or nondirectional hypothesis would then be considered alternative hypotheses to the null hypothesis.

Gill, J. (1999). The insignificance of null hypothesis significance testing.  Political research quarterly ,  52 (3), 647-674.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method.  American Psychologist ,  56 (1), 16.

Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.  Behavior research methods ,  43 , 679-690.

Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy.  Psychological methods ,  5 (2), 241.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.  Psychological bulletin ,  57 (5), 416.

• Translation

Responding to a Disproven or Failed Research Hypothesis

By charlesworth author services.

• Charlesworth Author Services
• 03 August, 2022

When meeting with a disproven or failed hypothesis , after having expended so much time and effort, precisely how should researchers respond? Responding well to a disproven or failed hypothesis is an essential component to scientific research . As a researcher, it helps to learn ‘ research resilience ’: the ability to carefully analyse, effectively document and broadly disseminate the failed hypotheses, all with an eye towards learning and future progress. This article explores common reasons why a hypothesis fails, as well as specific ways you can respond and lessons you can learn from this. 

Note : This article assumes that you are working on a hypothesis (not a null hypothesis): in other words, you are seeking to prove that the hypothesis is true, rather than to disprove it. 

Reasons why a hypothesis is disproven/fails

Hypotheses are disproved or fail for a number of reasons, including:

• The researcher’s preconception is incorrect , which leads to a flawed and failed hypothesis.
• The researcher’s findings are correct, but those findings aren’t relevant .
• Data set/sample size may not be sufficiently large to yield meaningful results. (If interested, learn more about this here: The importance of having Large Sample Sizes for your research )
• The hypothesis itself lies outside the realm of science . The hypothesis cannot be tested by experiments for which results have the potential to show that the idea is false.

Responding to a disproved hypothesis

After weeks or even months of intense thinking and experimenting, you have come to the conclusion that your hypothesis is disproven. So, what can you do to respond to such a disheartening realisation? Here are some practical steps you can take.

• Analyse the hypothesis carefully, as well as your research.   Performing a rigorous, methodical ‘post-mortem’ evaluation of your hypothesis and experiments will enable you to learn from them and to effectively and efficiently share your reflections with others. Use the following questions to evaluate how the research was conducted: 
• Did you conduct the experiment(s) correctly? 
• Was the study sufficiently powered to truly provide a definitive answer?
• Would a larger, better powered study – possibly conducted collaboratively with other research centres – be necessary, appropriate or helpful? 
• Would altering the experiment — or conducting different experiments — more appropriately answer your hypothesis? 
• Share the disproven hypothesis, and your experiments and analysis, with colleagues. Sharing negative data can help to interpret positive results from related studies and can aid you to adjust your experimental design .
• Consider the possibility that the hypothesis was not an attempt at gaining true scientific understanding, but rather, was a measure of a prevailing bias .

Positive lessons to be gained from a disproved hypothesis

Even the most successful, creative and thoughtful researchers encounter failed hypotheses. What makes them stand out is their ability to learn from failure. The following considerations may assist you to learn and gain from failed hypotheses:

• Failure can be beneficial if it leads directly toward future exploration.
• Does the failed hypothesis definitively close the door on further research? If so, such definitive knowledge is progress.
• Does the failed hypothesis simply point to the need to wait for a future date when more refined experiments or analysis can be conducted? That knowledge, too, is useful. 
• ‘Atomising’ (breaking down and dissecting) the reasoning behind the conceptual foundation of the failed hypothesis may uncover flawed yet correctable thinking in how the hypothesis was developed. 
• Failure leads to investigation and creativity in the pursuit of viable alternative hypotheses, experiments and statistical analyses. Better theoretical or experimental models often arise out of the ashes of a failed hypothesis, as do studies with more rigorously attained evidence (such as larger-scale, low-bias meta-analyses ). 

Considering a post-hoc analysis

A failed hypothesis can then prompt you to conduct a post-hoc analysis. (If interested, learn more about it here: Significance and use of Post-hoc Analysis studies )

All is not lost if you conclude you have a failed hypothesis. Remember: A hypothesis can’t be right unless it can be proven wrong.  Developing research resilience will reward you with long-term success.

Maximise your publication success with Charlesworth Author Services.

Charlesworth Author Services, a trusted brand supporting the world’s leading academic publishers, institutions and authors since 1928.

To know more about our services, visit: Our Services

Share with your colleagues

Scientific Editing Services

Sign up – stay updated.

We use cookies to offer you a personalized experience. By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Statistical Hypothesis Testing Overview

By Jim Frost 59 Comments

In this blog post, I explain why you need to use statistical hypothesis testing and help you navigate the essential terminology. Hypothesis testing is a crucial procedure to perform when you want to make inferences about a population using a random sample. These inferences include estimating population properties such as the mean, differences between means, proportions, and the relationships between variables.

This post provides an overview of statistical hypothesis testing. If you need to perform hypothesis tests, consider getting my book, Hypothesis Testing: An Intuitive Guide .

Why You Should Perform Statistical Hypothesis Testing

Hypothesis testing is a form of inferential statistics that allows us to draw conclusions about an entire population based on a representative sample. You gain tremendous benefits by working with a sample. In most cases, it is simply impossible to observe the entire population to understand its properties. The only alternative is to collect a random sample and then use statistics to analyze it.

While samples are much more practical and less expensive to work with, there are trade-offs. When you estimate the properties of a population from a sample, the sample statistics are unlikely to equal the actual population value exactly.  For instance, your sample mean is unlikely to equal the population mean. The difference between the sample statistic and the population value is the sample error.

Differences that researchers observe in samples might be due to sampling error rather than representing a true effect at the population level. If sampling error causes the observed difference, the next time someone performs the same experiment the results might be different. Hypothesis testing incorporates estimates of the sampling error to help you make the correct decision. Learn more about Sampling Error .

For example, if you are studying the proportion of defects produced by two manufacturing methods, any difference you observe between the two sample proportions might be sample error rather than a true difference. If the difference does not exist at the population level, you won’t obtain the benefits that you expect based on the sample statistics. That can be a costly mistake!

Let’s cover some basic hypothesis testing terms that you need to know.

Background information : Difference between Descriptive and Inferential Statistics and Populations, Parameters, and Samples in Inferential Statistics

Hypothesis Testing

Hypothesis testing is a statistical analysis that uses sample data to assess two mutually exclusive theories about the properties of a population. Statisticians call these theories the null hypothesis and the alternative hypothesis. A hypothesis test assesses your sample statistic and factors in an estimate of the sample error to determine which hypothesis the data support.

When you can reject the null hypothesis, the results are statistically significant, and your data support the theory that an effect exists at the population level.

The effect is the difference between the population value and the null hypothesis value. The effect is also known as population effect or the difference. For example, the mean difference between the health outcome for a treatment group and a control group is the effect.

Typically, you do not know the size of the actual effect. However, you can use a hypothesis test to help you determine whether an effect exists and to estimate its size. Hypothesis tests convert your sample effect into a test statistic, which it evaluates for statistical significance. Learn more about Test Statistics .

An effect can be statistically significant, but that doesn’t necessarily indicate that it is important in a real-world, practical sense. For more information, read my post about Statistical vs. Practical Significance .

Null Hypothesis

The null hypothesis is one of two mutually exclusive theories about the properties of the population in hypothesis testing. Typically, the null hypothesis states that there is no effect (i.e., the effect size equals zero). The null is often signified by H 0 .

In all hypothesis testing, the researchers are testing an effect of some sort. The effect can be the effectiveness of a new vaccination, the durability of a new product, the proportion of defect in a manufacturing process, and so on. There is some benefit or difference that the researchers hope to identify.

However, it’s possible that there is no effect or no difference between the experimental groups. In statistics, we call this lack of an effect the null hypothesis. Therefore, if you can reject the null, you can favor the alternative hypothesis, which states that the effect exists (doesn’t equal zero) at the population level.

You can think of the null as the default theory that requires sufficiently strong evidence against in order to reject it.

For example, in a 2-sample t-test, the null often states that the difference between the two means equals zero.

When you can reject the null hypothesis, your results are statistically significant. Learn more about Statistical Significance: Definition & Meaning .

Related post : Understanding the Null Hypothesis in More Detail

Alternative Hypothesis

The alternative hypothesis is the other theory about the properties of the population in hypothesis testing. Typically, the alternative hypothesis states that a population parameter does not equal the null hypothesis value. In other words, there is a non-zero effect. If your sample contains sufficient evidence, you can reject the null and favor the alternative hypothesis. The alternative is often identified with H 1 or H A .

For example, in a 2-sample t-test, the alternative often states that the difference between the two means does not equal zero.

You can specify either a one- or two-tailed alternative hypothesis:

If you perform a two-tailed hypothesis test, the alternative states that the population parameter does not equal the null value. For example, when the alternative hypothesis is H A : μ ≠ 0, the test can detect differences both greater than and less than the null value.

A one-tailed alternative has more power to detect an effect but it can test for a difference in only one direction. For example, H A : μ > 0 can only test for differences that are greater than zero.

Related posts : Understanding T-tests and One-Tailed and Two-Tailed Hypothesis Tests Explained

P-values are the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is correct. In simpler terms, p-values tell you how strongly your sample data contradict the null. Lower p-values represent stronger evidence against the null. You use P-values in conjunction with the significance level to determine whether your data favor the null or alternative hypothesis.

Related post : Interpreting P-values Correctly

Significance Level (Alpha)

For instance, a significance level of 0.05 signifies a 5% risk of deciding that an effect exists when it does not exist.

Use p-values and significance levels together to help you determine which hypothesis the data support. If the p-value is less than your significance level, you can reject the null and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to be able to reject the null hypothesis at the population level.

Related posts : Graphical Approach to Significance Levels and P-values and Conceptual Approach to Understanding Significance Levels

Types of Errors in Hypothesis Testing

Statistical hypothesis tests are not 100% accurate because they use a random sample to draw conclusions about entire populations. There are two types of errors related to drawing an incorrect conclusion.

• False positives: You reject a null that is true. Statisticians call this a Type I error . The Type I error rate equals your significance level or alpha (α).
• False negatives: You fail to reject a null that is false. Statisticians call this a Type II error. Generally, you do not know the Type II error rate. However, it is a larger risk when you have a small sample size , noisy data, or a small effect size. The type II error rate is also known as beta (β).

Statistical power is the probability that a hypothesis test correctly infers that a sample effect exists in the population. In other words, the test correctly rejects a false null hypothesis. Consequently, power is inversely related to a Type II error. Power = 1 – β. Learn more about Power in Statistics .

Related posts : Types of Errors in Hypothesis Testing and Estimating a Good Sample Size for Your Study Using Power Analysis

Which Type of Hypothesis Test is Right for You?

There are many different types of procedures you can use. The correct choice depends on your research goals and the data you collect. Do you need to understand the mean or the differences between means? Or, perhaps you need to assess proportions. You can even use hypothesis testing to determine whether the relationships between variables are statistically significant.

To choose the proper statistical procedure, you’ll need to assess your study objectives and collect the correct type of data . This background research is necessary before you begin a study.

Related Post : Hypothesis Tests for Continuous, Binary, and Count Data

Statistical tests are crucial when you want to use sample data to make conclusions about a population because these tests account for sample error. Using significance levels and p-values to determine when to reject the null hypothesis improves the probability that you will draw the correct conclusion.

To see an alternative approach to these traditional hypothesis testing methods, learn about bootstrapping in statistics !

If you want to see examples of hypothesis testing in action, I recommend the following posts that I have written:

• How Effective Are Flu Shots? This example shows how you can use statistics to test proportions.
• Fatality Rates in Star Trek . This example shows how to use hypothesis testing with categorical data.
• Busting Myths About the Battle of the Sexes . A fun example based on a Mythbusters episode that assess continuous data using several different tests.
• Are Yawns Contagious? Another fun example inspired by a Mythbusters episode.

Share this:

Reader Interactions

January 14, 2024 at 8:43 am

Hello professor Jim, how are you doing! Pls. What are the properties of a population and their examples? Thanks for your time and understanding.

January 14, 2024 at 12:57 pm

Please read my post about Populations vs. Samples for more information and examples.

Also, please note there is a search bar in the upper-right margin of my website. Use that to search for topics.

July 5, 2023 at 7:05 am

Hello, I have a question as I read your post. You say in p-values section

“P-values are the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is correct. In simpler terms, p-values tell you how strongly your sample data contradict the null. Lower p-values represent stronger evidence against the null.”

But according to your definition of effect, the null states that an effect does not exist, correct? So what I assume you want to say is that “P-values are the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is **incorrect**.”

July 6, 2023 at 5:18 am

Hi Shrinivas,

The correct definition of p-value is that it is a probability that exists in the context of a true null hypothesis. So, the quotation is correct in stating “if the null hypothesis is correct.”

Essentially, the p-value tells you the likelihood of your observed results (or more extreme) if the null hypothesis is true. It gives you an idea of whether your results are surprising or unusual if there is no effect.

Hence, with sufficiently low p-values, you reject the null hypothesis because it’s telling you that your sample results were unlikely to have occurred if there was no effect in the population.

I hope that helps make it more clear. If not, let me know I’ll attempt to clarify!

May 8, 2023 at 12:47 am

Thanks a lot Ny best regards

May 7, 2023 at 11:15 pm

Hi Jim Can you tell me something about size effect? Thanks

May 8, 2023 at 12:29 am

Here’s a post that I’ve written about Effect Sizes that will hopefully tell you what you need to know. Please read that. Then, if you have any more specific questions about effect sizes, please post them there. Thanks!

January 7, 2023 at 4:19 pm

Hi Jim, I have only read two pages so far but I am really amazed because in few paragraphs you made me clearly understand the concepts of months of courses I received in biostatistics! Thanks so much for this work you have done it helps a lot!

January 10, 2023 at 3:25 pm

Thanks so much!

June 17, 2021 at 1:45 pm

Can you help in the following question: Rocinante36 is priced at ₹7 lakh and has been designed to deliver a mileage of 22 km/litre and a top speed of 140 km/hr. Formulate the null and alternative hypotheses for mileage and top speed to check whether the new models are performing as per the desired design specifications.

April 19, 2021 at 1:51 pm

Its indeed great to read your work statistics.

I have a doubt regarding the one sample t-test. So as per your book on hypothesis testing with reference to page no 45, you have mentioned the difference between “the sample mean and the hypothesised mean is statistically significant”. So as per my understanding it should be quoted like “the difference between the population mean and the hypothesised mean is statistically significant”. The catch here is the hypothesised mean represents the sample mean.

Please help me understand this.

Regards Rajat

April 19, 2021 at 3:46 pm

Thanks for buying my book. I’m so glad it’s been helpful!

The test is performed on the sample but the results apply to the population. Hence, if the difference between the sample mean (observed in your study) and the hypothesized mean is statistically significant, that suggests that population does not equal the hypothesized mean.

For one sample tests, the hypothesized mean is not the sample mean. It is a mean that you want to use for the test value. It usually represents a value that is important to your research. In other words, it’s a value that you pick for some theoretical/practical reasons. You pick it because you want to determine whether the population mean is different from that particular value.

I hope that helps!

November 5, 2020 at 6:24 am

Jim, you are such a magnificent statistician/economist/econometrician/data scientist etc whatever profession. Your work inspires and simplifies the lives of so many researchers around the world. I truly admire you and your work. I will buy a copy of each book you have on statistics or econometrics. Keep doing the good work. Remain ever blessed

November 6, 2020 at 9:47 pm

Hi Renatus,

Thanks so much for you very kind comments. You made my day!! I’m so glad that my website has been helpful. And, thanks so much for supporting my books! 🙂

November 2, 2020 at 9:32 pm

Hi Jim, I hope you are aware of 2019 American Statistical Association’s official statement on Statistical Significance: https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 In case you do not bother reading the full article, may I quote you the core message here: “We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely. Nor should variants such as “significantly different,” “p < 0.05,” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way."

With best wishes,

November 3, 2020 at 2:09 am

I’m definitely aware of the debate surrounding how to use p-values most effectively. However, I need to correct you on one point. The link you provide is NOT a statement by the American Statistical Association. It is an editorial by several authors.

There is considerable debate over this issue. There are problems with p-values. However, as the authors state themselves, much of the problem is over people’s mindsets about how to use p-values and their incorrect interpretations about what statistical significance does and does not mean.

If you were to read my website more thoroughly, you’d be aware that I share many of their concerns and I address them in multiple posts. One of the authors’ key points is the need to be thoughtful and conduct thoughtful research and analysis. I emphasize this aspect in multiple posts on this topic. I’ll ask you to read the following three because they all address some of the authors’ concerns and suggestions. But you might run across others to read as well.

Five Tips for Using P-values to Avoid Being Misled How to Interpret P-values Correctly P-values and the Reproducibility of Experimental Results

September 24, 2020 at 11:52 pm

HI Jim, i just want you to know that you made explanation for Statistics so simple! I should say lesser and fewer words that reduce the complexity. All the best! 🙂

September 25, 2020 at 1:03 am

Thanks, Rene! Your kind words mean a lot to me! I’m so glad it has been helpful!

September 23, 2020 at 2:21 am

Honestly, I never understood stats during my entire M.Ed course and was another nightmare for me. But how easily you have explained each concept, I have understood stats way beyond my imagination. Thank you so much for helping ignorant research scholars like us. Looking forward to get hardcopy of your book. Kindly tell is it available through flipkart?

September 24, 2020 at 11:14 pm

I’m so happy to hear that my website has been helpful!

I checked on flipkart and it appears like my books are not available there. I’m never exactly sure where they’re available due to the vagaries of different distribution channels. They are available on Amazon in India.

Introduction to Statistics: An Intuitive Guide (Amazon IN) Hypothesis Testing: An Intuitive Guide (Amazon IN)

July 26, 2020 at 11:57 am

Dear Jim I am a teacher from India . I don’t have any background in statistics, and still I should tell that in a single read I can follow your explanations . I take my entire biostatistics class for botany graduates with your explanations. Thanks a lot. May I know how I can avail your books in India

July 28, 2020 at 12:31 am

Right now my books are only available as ebooks from my website. However, soon I’ll have some exciting news about other ways to obtain it. Stay tuned! I’ll announce it on my email list. If you’re not already on it, you can sign up using the form that is in the right margin of my website.

June 22, 2020 at 2:02 pm

Also can you please let me if this book covers topics like EDA and principal component analysis?

June 22, 2020 at 2:07 pm

This book doesn’t cover principal components analysis. Although, I wouldn’t really classify that as a hypothesis test. In the future, I might write a multivariate analysis book that would cover this and others. But, that’s well down the road.

My Introduction to Statistics covers EDA. That’s the largely graphical look at your data that you often do prior to hypothesis testing. The Introduction book perfectly leads right into the Hypothesis Testing book.

June 22, 2020 at 1:45 pm

Thanks for the detailed explanation. It does clear my doubts. I saw that your book related to hypothesis testing has the topics that I am studying currently. I am looking forward to purchasing it.

Regards, Take Care

June 19, 2020 at 1:03 pm

For this particular article I did not understand a couple of statements and it would great if you could help: 1)”If sample error causes the observed difference, the next time someone performs the same experiment the results might be different.” 2)”If the difference does not exist at the population level, you won’t obtain the benefits that you expect based on the sample statistics.”

I discovered your articles by chance and now I keep coming back to read & understand statistical concepts. These articles are very informative & easy to digest. Thanks for the simplifying things.

June 20, 2020 at 9:53 pm

I’m so happy to hear that you’ve found my website to be helpful!

To answer your questions, keep in mind that a central tenant of inferential statistics is that the random sample that a study drew was only one of an infinite number of possible it could’ve drawn. Each random sample produces different results. Most results will cluster around the population value assuming they used good methodology. However, random sampling error always exists and makes it so that population estimates from a sample almost never exactly equal the correct population value.

So, imagine that we’re studying a medication and comparing the treatment and control groups. Suppose that the medicine is truly not effect and that the population difference between the treatment and control group is zero (i.e., no difference.) Despite the true difference being zero, most sample estimates will show some degree of either a positive or negative effect thanks to random sampling error. So, just because a study has an observed difference does not mean that a difference exists at the population level. So, on to your questions:

1. If the observed difference is just random error, then it makes sense that if you collected another random sample, the difference could change. It could change from negative to positive, positive to negative, more extreme, less extreme, etc. However, if the difference exists at the population level, most random samples drawn from the population will reflect that difference. If the medicine has an effect, most random samples will reflect that fact and not bounce around on both sides of zero as much.

2. This is closely related to the previous answer. If there is no difference at the population level, but say you approve the medicine because of the observed effects in a sample. Even though your random sample showed an effect (which was really random error), that effect doesn’t exist. So, when you start using it on a larger scale, people won’t benefit from the medicine. That’s why it’s important to separate out what is easily explained by random error versus what is not easily explained by it.

I think reading my post about how hypothesis tests work will help clarify this process. Also, in about 24 hours (as I write this), I’ll be releasing my new ebook about Hypothesis Testing!

May 29, 2020 at 5:23 am

Hi Jim, I really enjoy your blog. Can you please link me on your blog where you discuss about Subgroup analysis and how it is done? I need to use non parametric and parametric statistical methods for my work and also do subgroup analysis in order to identify potential groups of patients that may benefit more from using a treatment than other groups.

May 29, 2020 at 2:12 pm

Hi, I don’t have a specific article about subgroup analysis. However, subgroup analysis is just the dividing up of a larger sample into subgroups and then analyzing those subgroups separately. You can use the various analyses I write about on the subgroups.

Alternatively, you can include the subgroups in regression analysis as an indicator variable and include that variable as a main effect and an interaction effect to see how the relationships vary by subgroup without needing to subdivide your data. I write about that approach in my article about comparing regression lines . This approach is my preferred approach when possible.

April 19, 2020 at 7:58 am

sir is confidence interval is a part of estimation?

April 17, 2020 at 3:36 pm

Sir can u plz briefly explain alternatives of hypothesis testing? I m unable to find the answer

April 18, 2020 at 1:22 am

Assuming you want to draw conclusions about populations by using samples (i.e., inferential statistics ), you can use confidence intervals and bootstrap methods as alternatives to the traditional hypothesis testing methods.

March 9, 2020 at 10:01 pm

Hi JIm, could you please help with activities that can best teach concepts of hypothesis testing through simulation, Also, do you have any question set that would enhance students intuition why learning hypothesis testing as a topic in introductory statistics. Thanks.

March 5, 2020 at 3:48 pm

Hi Jim, I’m studying multiple hypothesis testing & was wondering if you had any material that would be relevant. I’m more trying to understand how testing multiple samples simultaneously affects your results & more on the Bonferroni Correction

March 5, 2020 at 4:05 pm

I write about multiple comparisons (aka post hoc tests) in the ANOVA context . I don’t talk about Bonferroni Corrections specifically but I cover related types of corrections. I’m not sure if that exactly addresses what you want to know but is probably the closest I have already written. I hope it helps!

January 14, 2020 at 9:03 pm

Thank you! Have a great day/evening.

January 13, 2020 at 7:10 pm

Any help would be greatly appreciated. What is the difference between The Hypothesis Test and The Statistical Test of Hypothesis?

January 14, 2020 at 11:02 am

They sound like the same thing to me. Unless this is specialized terminology for a particular field or the author was intending something specific, I’d guess they’re one and the same.

April 1, 2019 at 10:00 am

so these are the only two forms of Hypothesis used in statistical testing?

April 1, 2019 at 10:02 am

Are you referring to the null and alternative hypothesis? If so, yes, that’s those are the standard hypotheses in a statistical hypothesis test.

April 1, 2019 at 9:57 am

year very insightful post, thanks for the write up

October 27, 2018 at 11:09 pm

hi there, am upcoming statistician, out of all blogs that i have read, i have found this one more useful as long as my problem is concerned. thanks so much

October 27, 2018 at 11:14 pm

Hi Stano, you’re very welcome! Thanks for your kind words. They mean a lot! I’m happy to hear that my posts were able to help you. I’m sure you will be a fantastic statistician. Best of luck with your studies!

October 26, 2018 at 11:39 am

Dear Jim, thank you very much for your explanations! I have a question. Can I use t-test to compare two samples in case each of them have right bias?

October 26, 2018 at 12:00 pm

Hi Tetyana,

You’re very welcome!

The term “right bias” is not a standard term. Do you by chance mean right skewed distributions? In other words, if you plot the distribution for each group on a histogram they have longer right tails? These are not the symmetrical bell-shape curves of the normal distribution.

If that’s the case, yes you can as long as you exceed a specific sample size within each group. I include a table that contains these sample size requirements in my post about nonparametric vs parametric analyses .

Bias in statistics refers to cases where an estimate of a value is systematically higher or lower than the true value. If this is the case, you might be able to use t-tests, but you’d need to be sure to understand the nature of the bias so you would understand what the results are really indicating.

I hope this helps!

April 2, 2018 at 7:28 am

Simple and upto the point 👍 Thank you so much.

April 2, 2018 at 11:11 am

Hi Kalpana, thanks! And I’m glad it was helpful!

March 26, 2018 at 8:41 am

Am I correct if I say: Alpha – Probability of wrongly rejection of null hypothesis P-value – Probability of wrongly acceptance of null hypothesis

March 28, 2018 at 3:14 pm

You’re correct about alpha. Alpha is the probability of rejecting the null hypothesis when the null is true.

Unfortunately, your definition of the p-value is a bit off. The p-value has a fairly convoluted definition. It is the probability of obtaining the effect observed in a sample, or more extreme, if the null hypothesis is true. The p-value does NOT indicate the probability that either the null or alternative is true or false. Although, those are very common misinterpretations. To learn more, read my post about how to interpret p-values correctly .

March 2, 2018 at 6:10 pm

I recently started reading your blog and it is very helpful to understand each concept of statistical tests in easy way with some good examples. Also, I recommend to other people go through all these blogs which you posted. Specially for those people who have not statistical background and they are facing to many problems while studying statistical analysis.

Thank you for your such good blogs.

March 3, 2018 at 10:12 pm

Hi Amit, I’m so glad that my blog posts have been helpful for you! It means a lot to me that you took the time to write such a nice comment! Also, thanks for recommending by blog to others! I try really hard to write posts about statistics that are easy to understand.

January 17, 2018 at 7:03 am

I recently started reading your blog and I find it very interesting. I am learning statistics by my own, and I generally do many google search to understand the concepts. So this blog is quite helpful for me, as it have most of the content which I am looking for.

January 17, 2018 at 3:56 pm

Hi Shashank, thank you! And, I’m very glad to hear that my blog is helpful!

January 2, 2018 at 2:28 pm

thank u very much sir.

January 2, 2018 at 2:36 pm

You’re very welcome, Hiral!

November 21, 2017 at 12:43 pm

Thank u so much sir….your posts always helps me to be a #statistician

November 21, 2017 at 2:40 pm

Hi Sachin, you’re very welcome! I’m happy that you find my posts to be helpful!

November 19, 2017 at 8:22 pm

great post as usual, but it would be nice to see an example.

November 19, 2017 at 8:27 pm

Thank you! At the end of this post, I have links to four other posts that show examples of hypothesis tests in action. You’ll find what you’re looking for in those posts!

Comments and Questions Cancel reply

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

• Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
• Duis aute irure dolor in reprehenderit in voluptate
• Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

6a.1 - introduction to hypothesis testing, basic terms section  .

The first step in hypothesis testing is to set up two competing hypotheses. The hypotheses are the most important aspect. If the hypotheses are incorrect, your conclusion will also be incorrect.

The two hypotheses are named the null hypothesis and the alternative hypothesis.

The goal of hypothesis testing is to see if there is enough evidence against the null hypothesis. In other words, to see if there is enough evidence to reject the null hypothesis. If there is not enough evidence, then we fail to reject the null hypothesis.

Consider the following example where we set up these hypotheses.

Example 6-1 Section  

A man, Mr. Orangejuice, goes to trial and is tried for the murder of his ex-wife. He is either guilty or innocent. Set up the null and alternative hypotheses for this example.

Putting this in a hypothesis testing framework, the hypotheses being tested are:

• The man is guilty
• The man is innocent

Let's set up the null and alternative hypotheses.

\(H_0\colon \) Mr. Orangejuice is innocent

\(H_a\colon \) Mr. Orangejuice is guilty

Remember that we assume the null hypothesis is true and try to see if we have evidence against the null. Therefore, it makes sense in this example to assume the man is innocent and test to see if there is evidence that he is guilty.

The Logic of Hypothesis Testing Section  

We want to know the answer to a research question. We determine our null and alternative hypotheses. Now it is time to make a decision.

The decision is either going to be...

• reject the null hypothesis or...
• fail to reject the null hypothesis.

Consider the following table. The table shows the decision/conclusion of the hypothesis test and the unknown "reality", or truth. We do not know if the null is true or if it is false. If the null is false and we reject it, then we made the correct decision. If the null hypothesis is true and we fail to reject it, then we made the correct decision.

So what happens when we do not make the correct decision?

When doing hypothesis testing, two types of mistakes may be made and we call them Type I error and Type II error. If we reject the null hypothesis when it is true, then we made a type I error. If the null hypothesis is false and we failed to reject it, we made another error called a Type II error.

Types of errors

The “reality”, or truth, about the null hypothesis is unknown and therefore we do not know if we have made the correct decision or if we committed an error. We can, however, define the likelihood of these events.

\(\alpha\) and \(\beta\) are probabilities of committing an error so we want these values to be low. However, we cannot decrease both. As \(\alpha\) decreases, \(\beta\) increases.

Example 6-1 Cont'd... Section  

A man, Mr. Orangejuice, goes to trial and is tried for the murder of his ex-wife. He is either guilty or not guilty. We found before that...

• \( H_0\colon \) Mr. Orangejuice is innocent
• \( H_a\colon \) Mr. Orangejuice is guilty

Interpret Type I error, \(\alpha \), Type II error, \(\beta \).

As you can see here, the Type I error (putting an innocent man in jail) is the more serious error. Ethically, it is more serious to put an innocent man in jail than to let a guilty man go free. So to minimize the probability of a type I error we would choose a smaller significance level.

Try it! Section  

An inspector has to choose between certifying a building as safe or saying that the building is not safe. There are two hypotheses:

• Building is safe
• Building is not safe

Set up the null and alternative hypotheses. Interpret Type I and Type II error.

\( H_0\colon\) Building is not safe vs \(H_a\colon \) Building is safe

Power and \(\beta \) are complements of each other. Therefore, they have an inverse relationship, i.e. as one increases, the other decreases.

• Anatomy & Physiology
• Astrophysics
• Earth Science
• Environmental Science
• Organic Chemistry
• Precalculus
• Trigonometry
• English Grammar
• U.S. History
• World History

... and beyond

• Socratic Meta
• Featured Answers

Sometimes your data will not support your hypothesis. If this is the the case, what should you?

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Turney, S. (2023, June 22). Null & Alternative Hypotheses | Definitions, Templates & Examples. Scribbr. Retrieved July 22, 2024, from https://www.scribbr.com/statistics/null-and-alternative-hypotheses/

Is this article helpful?

Shaun Turney

Other students also liked, inferential statistics | an easy introduction & examples, hypothesis testing | a step-by-step guide with easy examples, type i & type ii errors | differences, examples, visualizations, what is your plagiarism score.

Back to blog home

Hypothesis testing explained in 4 parts, yuzheng sun, phd.

As data scientists, Hypothesis Testing is expected to be well understood, but often not in reality. It is mainly because our textbooks blend two schools of thought – p-value and significance testing vs. hypothesis testing – inconsistently.

For example, some questions are not obvious unless you have thought through them before:

Are power or beta dependent on the null hypothesis?

Can we accept the null hypothesis? Why?

How does MDE change with alpha holding beta constant?

Why do we use standard error in Hypothesis Testing but not the standard deviation?

Why can’t we be specific about the alternative hypothesis so we can properly model it?

Why is the fundamental tradeoff of the Hypothesis Testing about mistake vs. discovery, not about alpha vs. beta?

Addressing this problem is not easy. The topic of Hypothesis Testing is convoluted. In this article, there are 10 concepts that we will introduce incrementally, aid you with visualizations, and include intuitive explanations. After this article, you will have clear answers to the questions above that you truly understand on a first-principle level and explain these concepts well to your stakeholders.

We break this article into four parts.

Set up the question properly using core statistical concepts, and connect them to Hypothesis Testing, while striking a balance between technically correct and simplicity. Specifically, 

We emphasize a clear distinction between the standard deviation and the standard error, and why the latter is used in Hypothesis Testing

We explain fully when can you “accept” a hypothesis, when shall you say “failing to reject” instead of “accept”, and why

Introduce alpha, type I error, and the critical value with the null hypothesis

Introduce beta, type II error, and power with the alternative hypothesis

Introduce minimum detectable effects and the relationship between the factors with power calculations , with a high-level summary and practical recommendations

Part 1 - Hypothesis Testing, the central limit theorem, population, sample, standard deviation, and standard error

In Hypothesis Testing, we begin with a null hypothesis , which generally asserts that there is no effect between our treatment and control groups. Commonly, this is expressed as the difference in means between the treatment and control groups being zero.

The central limit theorem suggests an important property of this difference in means — given a sufficiently large sample size, the underlying distribution of this difference in means will approximate a normal distribution, regardless of the population's original distribution. There are two notes:

1. The distribution of the population for the treatment and control groups can vary, but the observed means (when you observe many samples and calculate many means) are always normally distributed with a large enough sample. Below is a chart, where the n=10 and n=30 correspond to the underlying distribution of the sample means.

2. Pay attention to “the underlying distribution”. Standard deviation vs. standard error is a potentially confusing concept. Let’s clarify.

Standard deviation vs. Standard error

Let’s declare our null hypothesis as having no treatment effect. Then, to simplify, let’s propose the following normal distribution with a mean of 0 and a standard deviation of 1 as the range of possible outcomes with probabilities associated with this null hypothesis.

The language around population, sample, group, and estimators can get confusing. Again, to simplify, let’s forget that the null hypothesis is about the mean estimator, and declare that we can either observe the mean hypothesis once or many times. When we observe it many times, it forms a sample*, and our goal is to make decisions based on this sample.

* For technical folks, the observation is actually about a single sample, many samples are a group, and the difference in groups is the distribution we are talking about as the mean hypothesis. The red curve represents the distribution of the estimator of this difference, and then we can have another sample consisting of many observations of this estimator. In my simplified language, the red curve is the distribution of the estimator, and the blue curve with sample size is the repeated observations of it. If you have a better way to express these concepts without causing confusiongs, please suggest.

This probability density function means if there is one realization from this distribution, the realitization can be anywhere on the x-axis, with the relative likelihood on the y-axis.

If we draw multiple observations , they form a sample . Each observation in this sample follows the property of this underlying distribution – more likely to be close to 0, and equally likely to be on either side, which makes the odds of positive and negative cancel each other out, so the mean of this sample is even more centered around 0.

We use the standard error to represent the error of our “sample mean” . 

The standard error = the standard deviation of the observed sample / sqrt (sample size). 

For a sample size of 30, the standard error is roughly 0.18. Compared with the underlying distribution, the distribution of the sample mean is much narrower.

In Hypothesis Testing, we try to draw some conclusions – is there a treatment effect or not? – based on a sample. So when we talk about alpha and beta, which are the probabilities of type I and type II errors , we are talking about the probabilities based on the plot of sample means and standard error .

Part 2, The null hypothesis: alpha and the critical value

From Part 1, we stated that a null hypothesis is commonly expressed as the difference in means between the treatment and control groups being zero.

Without loss of generality*, let’s assume the underlying distribution of our null hypothesis is mean 0 and standard deviation 1

Then the sample mean of the null hypothesis is 0 and the standard error of 1/√ n, where n is the sample size.

When the sample size is 30, this distribution has a standard error of ≈0.18 looks like the below. 

*: A note for the technical readers: The null hypothesis is about the difference in means, but here, without complicating things, we made the subtle change to just draw the distribution of this “estimator of this difference in means”. Everything below speaks to this “estimator”.

The reason we have the null hypothesis is that we want to make judgments, particularly whether a  treatment effect exists. But in the world of probabilities, any observation, and any sample mean can happen, with different probabilities. So we need a decision rule to help us quantify our risk of making mistakes.

The decision rule is, let’s set a threshold. When the sample mean is above the threshold, we reject the null hypothesis; when the sample mean is below the threshold, we accept the null hypothesis.

Accepting a hypothesis vs. failing to reject a hypothesis

It’s worth noting that you may have heard of “we never accept a hypothesis, we just fail to reject a hypothesis” and be subconsciously confused by it. The deep reason is that modern textbooks do an inconsistent blend of Fisher’s significance testing and Neyman-Pearson’s Hypothesis Testing definitions and ignore important caveats ( ref ). To clarify:

First of all, we can never “prove” a particular hypothesis given any observations, because there are infinitely many true hypotheses (with different probabilities) given an observation. We will visualize it in Part 3.

Second, “accepting” a hypothesis does not mean that you believe in it, but only that you act as if it were true. So technically, there is no problem with “accepting” a hypothesis.

But, third, when we talk about p-values and confidence intervals, “accepting” the null hypothesis is at best confusing. The reason is that “the p-value above the threshold” just means we failed to reject the null hypothesis. In the strict Fisher’s p-value framework, there is no alternative hypothesis. While we have a clear criterion for rejecting the null hypothesis (p < alpha), we don't have a similar clear-cut criterion for "accepting" the null hypothesis based on beta.

So the dangers in calling “accepting a hypothesis” in the p-value setting are:

Many people misinterpret “accepting” the null hypothesis as “proving” the null hypothesis, which is wrong; 

“Accepting the null hypothesis” is not rigorously defined, and doesn’t speak to the purpose of the test, which is about whether or not we reject the null hypothesis. 

In this article, we will stay consistent within the Neyman-Pearson framework , where “accepting” a hypothesis is legal and necessary. Otherwise, we cannot draw any distributions without acting as if some hypothesis was true.

You don’t need to know the name Neyman-Pearson to understand anything, but pay attention to our language, as we choose our words very carefully to avoid mistakes and confusion.

So far, we have constructed a simple world of one hypothesis as the only truth, and a decision rule with two potential outcomes – one of the outcomes is “reject the null hypothesis when it is true” and the other outcome is “accept the null hypothesis when it is true”. The likelihoods of both outcomes come from the distribution where the null hypothesis is true.

Later, when we introduce the alternative hypothesis and MDE, we will gradually walk into the world of infinitely many alternative hypotheses and visualize why we cannot “prove” a hypothesis.

We save the distinction between the p-value/significance framework vs. Hypothesis Testing in another article where you will have the full picture.

Type I error, alpha, and the critical value

We’re able to construct a distribution of the sample mean for this null hypothesis using the standard error. Since we only have the null hypothesis as the truth of our universe, we can only make one type of mistake – falsely rejecting the null hypothesis when it is true. This is the type I error , and the probability is called alpha . Suppose we want alpha to be 5%. We can calculate the threshold required to make it happen. This threshold is called the critical value . Below is the chart we further constructed with our sample of 30.

In this chart, alpha is the blue area under the curve. The critical value is 0.3. If our sample mean is above 0.3, we reject the null hypothesis. We have a 5% chance of making the type I error.

Type I error: Falsely rejecting the null hypothesis when the null hypothesis is true

Alpha: The probability of making a Type I error

Critical value: The threshold to determine whether the null hypothesis is to be rejected or not

Part 3, The alternative hypothesis: beta and power

You may have noticed in part 2 that we only spoke to Type I error – rejecting the null hypothesis when it is true. What about the Type II error – falsely accepting the null hypothesis when it is not true?

But it is weird to call “accepting” false unless we know the truth. So we need an alternative hypothesis which serves as the alternative truth. 

Alternative hypotheses are theoretical constructs

There is an important concept that most textbooks fail to emphasize – that is, you can have infinitely many alternative hypotheses for a given null hypothesis, we just choose one. None of them are more special or “real” than the others. 

Let’s visualize it with an example. Suppose we observed a sample mean of 0.51, what is the true alternative hypothesis?

With this visualization, you can see why we have “infinitely many alternative hypotheses” because, given the observation, there is an infinite number of alternative hypotheses (plus the null hypothesis) that can be true, each with different probabilities. Some are more likely than others, but all are possible.

Remember, alternative hypotheses are a theoretical construct. We choose one particular alternative hypothesis to calculate certain probabilities. By now, we should have more understanding of why we cannot “accept” the null hypothesis given an observation. We can’t prove that the null hypothesis is true, we just fail to accept it given the observation and our pre-determined decision rule. 

We will fully reconcile this idea of picking one alternative hypothesis out of the world of infinite possibilities when we talk about MDE. The idea of “accept” vs. “fail to reject” is deeper, and we won’t cover it fully in this article. We will do so when we have an article about the p-value and the confidence interval.

Type II error and Beta

For the sake of simplicity and easy comparison, let’s choose an alternative hypothesis with a mean of 0.5, and a standard deviation of

1. Again, with a sample size of 30, the standard error ≈0.18. There are now two potential “truths” in our simple universe.

Remember from the null hypothesis, we want alpha to be 5% so the corresponding critical value is 0.30. We modify our rule as follows:

If the observation is above 0.30, we reject the null hypothesis and accept the alternative hypothesis ; 

If the observation is below 0.30, we accept the null hypothesis and reject the alternative hypothesis .

With the introduction of the alternative hypothesis, the alternative “(hypothesized) truth”, we can call “accepting the null hypothesis and rejecting the alternative hypothesis” a mistake – the Type II error. We can also calculate the probability of this mistake. This is called beta, which is illustrated by the red area below.

From the visualization, we can see that beta is conditional on the alternative hypothesis and the critical value. Let’s elaborate on these two relationships one by one, very explicitly, as both of them are important.

First, Let’s visualize how beta changes with the mean of the alternative hypothesis by setting another alternative hypothesis where mean = 1 instead of 0.5

Beta change from 13.7% to 0.0%. Namely, beta is the probability of falsely rejecting a particular alternative hypothesis when we assume it is true. When we assume a different alternative hypothesis is true, we get a different beta. So strictly speaking, beta only speaks to the probability of falsely rejecting a particular alternative hypothesis when it is true . Nothing else. It’s only under other conditions, that “rejecting the alternative hypothesis” implies “accepting” the null hypothesis or “failing to accept the null hypothesis”. We will further elaborate when we talk about p-value and confidence interval in another article. But what we talked about so far is true and enough for understanding power.

Second, there is a relationship between alpha and beta. Namely, given the null hypothesis and the alternative hypothesis, alpha would determine the critical value, and the critical value determines beta. This speaks to the tradeoff between mistake and discovery. 

If we tolerate more alpha, we will have a smaller critical value, and for the same beta, we can detect a smaller alternative hypothesis

If we tolerate more beta, we can also detect a smaller alternative hypothesis. 

In short, if we tolerate more mistakes (either Type I or Type II), we can detect a smaller true effect. Mistake vs. discovery is the fundamental tradeoff of Hypothesis Testing.

So tolerating more mistakes leads to more chance of discovery. This is the concept of MDE that we will elaborate on in part 4.

Finally, we’re ready to define power. Power is an important and fundamental topic in statistical testing, and we’ll explain the concept in three different ways.

Three ways to understand power

First, the technical definition of power is 1−β. It represents that given an alternative hypothesis and given our null, sample size, and decision rule (alpha = 0.05), the probability is that we accept this particular hypothesis. We visualize the yellow area below.

Second, power is really intuitive in its definition. A real-world example is trying to determine the most popular car manufacturer in the world. If I observe one car and see one brand, my observation is not very powerful. But if I observe a million cars, my observation is very powerful. Powerful tests mean that I have a high chance of detecting a true effect.

Third, to illustrate the two concepts concisely, let’s run a visualization by just changing the sample size from 30 to 100 and see how power increases from 86.3% to almost 100%.

As the graph shows, we can easily see that power increases with sample size . The reason is that the distribution of both the null hypothesis and the alternative hypothesis became narrower as their sample means got more accurate. We are less likely to make either a type I error (which reduces the critical value) or a type II error.  

Type II error: Failing to reject the null hypothesis when the alternative hypothesis is true

Beta: The probability of making a type II error

Power: The ability of the test to detect a true effect when it’s there

Part 4, Power calculation: MDE

The relationship between mde, alternative hypothesis, and power.

Now, we are ready to tackle the most nuanced definition of them all: Minimum detectable effect (MDE). First, let’s make the sample mean of the alternative hypothesis explicit on the graph with a red dotted line.

What if we keep the same sample size, but want power to be 80%? This is when we recall the previous chapter that “alternative hypotheses are theoretical constructs”. We can have a different alternative that corresponds to 80% power. After some calculations, we discovered that when it’s the alternative hypothesis with mean = 0.45 (if we keep the standard deviation to be 1).

This is where we reconcile the concept of “infinitely many alternative hypotheses” with the concept of minimum detectable delta. Remember that in statistical testing, we want more power. The “ minimum ” in the “ minimum detectable effect”, is the minimum value of the mean of the alternative hypothesis that would give us 80% power. Any alternative hypothesis with a mean to the right of MDE gives us sufficient power.

In other words, there are indeed infinitely many alternative hypotheses to the right of this mean 0.45. The particular alternative hypothesis with a mean of 0.45 gives us the minimum value where power is sufficient. We call it the minimum detectable effect, or MDE.

The complete definition of MDE from scratch

Let’s go through how we derived MDE from the beginning:

We fixed the distribution of sample means of the null hypothesis, and fixed sample size, so we can draw the blue distribution

For our decision rule, we require alpha to be 5%. We derived that the critical value shall be 0.30 to make 5% alpha happen

We fixed the alternative hypothesis to be normally distributed with a standard deviation of 1 so the standard error is 0.18, the mean can be anywhere as there are infinitely many alternative hypotheses

For our decision rule, we require beta to be 20% or less, so our power is 80% or more. 

We derived that the minimum value of the observed mean of the alternative hypothesis that we can detect with our decision rule is 0.45. Any value above 0.45 would give us sufficient power.

How MDE changes with sample size

Now, let’s tie everything together by increasing the sample size, holding alpha and beta constant, and see how MDE changes.

Narrower distribution of the sample mean + holding alpha constant -> smaller critical value from 0.3 to 0.16

+ holding beta constant -> MDE decreases from 0.45 to 0.25

This is the other key takeaway:  The larger the sample size, the smaller of an effect we can detect, and the smaller the MDE.

This is a critical takeaway for statistical testing. It suggests that even for companies not with large sample sizes if their treatment effects are large, AB testing can reliably detect it.

Summary of Hypothesis Testing

Let’s review all the concepts together.

Assuming the null hypothesis is correct:

Alpha: When the null hypothesis is true, the probability of rejecting it

Critical value: The threshold to determine rejecting vs. accepting the null hypothesis

Assuming an alternative hypothesis is correct:

Beta: When the alternative hypothesis is true, the probability of rejecting it

Power: The chance that a real effect will produce significant results

Power calculation:

Minimum detectable effect (MDE): Given sample sizes and distributions, the minimum mean of alternative distribution that would give us the desired alpha and sufficient power (usually alpha = 0.05 and power >= 0.8)

Relationship among the factors, all else equal: Larger sample, more power; Larger sample, smaller MDE

Everything we talk about is under the Neyman-Pearson framework. There is no need to mention the p-value and significance under this framework. Blending the two frameworks is the inconsistency brought by our textbooks. Clarifying the inconsistency and correctly blending them are topics for another day.

Practical recommendations

That’s it. But it’s only the beginning. In practice, there are many crafts in using power well, for example:

Why peeking introduces a behavior bias, and how to use sequential testing to correct it

Why having multiple comparisons affects alpha, and how to use Bonferroni correction

The relationship between sample size, duration of the experiment, and allocation of the experiment?

Treat your allocation as a resource for experimentation, understand when interaction effects are okay, and when they are not okay, and how to use layers to manage

Practical considerations for setting an MDE

Also, in the above examples, we fixed the distribution, but in reality, the variance of the distribution plays an important role. There are different ways of calculating the variance and different ways to reduce variance, such as CUPED, or stratified sampling.

Related resources:

How to calculate power with an uneven split of sample size: https://blog.statsig.com/calculating-sample-sizes-for-a-b-tests-7854d56c2646

Real-life applications: https://blog.statsig.com/you-dont-need-large-sample-sizes-to-run-a-b-tests-6044823e9992

Create a free account

2m events per month, free forever..

Sign up for Statsig and launch your first experiment in minutes.

Build fast?

Try statsig today.

Recent Posts

Top 8 common experimentation mistakes and how to fix them.

I discussed 8 A/B testing mistakes with Allon Korem (Bell Statistics) and Tyler VanHaren (Statsig). Learn fixes to improve accuracy and drive better business outcomes.

Introducing Differential Impact Detection

Introducing Differential Impact Detection: Identify how different user groups respond to treatments and gain useful insights from varied experiment results.

Identifying and experimenting with Power Users using Statsig

Identify power users to drive growth and engagement. Learn to pinpoint and leverage these key players with targeted experiments for maximum impact.

How to Ingest Data Into Statsig

Simplify data pipelines with Statsig. Use SDKs, third-party integrations, and Data Warehouse Native Solution for effortless data ingestion at any stage.

A/B Testing performance wins on NestJS API servers

Learn how we use Statsig to enhance our NestJS API servers, reducing request processing time and CPU usage through performance experiments.

An overview of making early decisions on experiments

Learn the risks vs. rewards of making early decisions in experiments and Statsig's techniques to reduce experimentation times and deliver trustworthy results.

If the results of an experiment do not support a scientist's hypothesis, what should the scientist do next? O A. Revise the hypothesis based on the results and use it to design another experiment. B. Change the data so that they support the original hypothesis. OC. Abandon the research and investigate a different topic. OD. Accept that the investigation failed and publish the results for peers to review.​

A. Revise the hypothesis based on the results and use it to design another experiment.

Explanation:

Related Questions

What could happen if a scientist discovered new information concerning a scientific theory? Nothing would change since laws and theories have completely different components and are not interchangeable. The new information could be used to create another, slightly different theory. The new information could be used to change the theory into a law. Nothing would change since laws and theories have similar components and are interchangeable

If a scientist discovered new information concerning a scientific theory , the new information could be used to change the theory into a law.

The scientific theory is a carefully thought-out explanation for observations of the natural world that has been constructed using the scientific method, and which brings together many facts and hypotheses.

It is an explanation of an aspect of the natural world and universe that has been repeatedly tested and corroborated.

It refers to is a well-substantiated explanation of some aspect of the natural world which is based on a body of facts that have been repeatedly confirmed through observation and experiment. A scientific theory is not the end result of the scientific method.

It can be proven or rejected, as hypotheses.

As theories are continually improved or modified when more information is gathered, the accuracy of the prediction becomes greater over time.

Hence, if a scientist discovered new information concerning a scientific theory, the new information could be used to change the theory into a law.

Learn more about scientific theory , click here https://brainly.com/question/2375277

Which of the following is a description of a resource that belongs to a country with a high carrying capacity? A. Water comes directly from bodies of water. B. Homes are small and house many people. C. Food is brought in from other places. D. Food is not always avail able.​

food and water can be obtained by either hunting or farming but small houses more people is a common problem in bigger countries due to new infrastructural ideas.

pls mark as brainliest

Scientists observe the layers for evidence of similar rock types and fossil formations to determine the age of landforms. If they find the same fossil in two different locations, scientists may conclude that those fossils are most likely from the same time period. Fossils are also used as evidence to show changes that have occurred in living organisms throughout geologic time. Some organisms that are now extinct have left their fossil evidence, allowing scientists to see the ancestry and relationships to organisms of today.

All examples above are types of scientific evidence that involves features of dead organisms through fossils (evolutionary trajectories), Earth age , and/or rock formation .

The scientific method i s a series of scientific steps used to collect scientific evidence through observation and/or experimental procedures.

For example, the observation of the same fossils in different locations in Earth's sediment layer is a type of scientific evidence involving the same geological period .

In conclusion, All examples above are types of scientific evidence that involves features of dead organisms through fossils (evolutionary trajectories), Earth age , and/or rock formation .

Learn more about scientific evidence here:

https://brainly.com/question/507522

What statement accurately compares sexual and asexual reproduction? Answer: CORRECT (SELECTED) Sexual reproduction results in genetic variation and asexual reproduction does not. Explanation: In sexual reproduction, DNA of two gametes is combined, resulting in genetically-diverse offspring. Offspring of asexual reproduction are identical to their parents.

Sexual reproduction results in genetic variation and asexual reproduction does not.

The key difference between sexual and asexual reproduction is that sexual reproduction occurs between two parents while asexual reproduction occurs via a single parent. Asexual reproduction requires only a single divisible cell to produce a new organism, whereas sexual reproduction requires two gametes, their formation and fusion.

Answer:you answered:)

Explanation:Sexual reproduction results in genetic variation and asexual reproduction does not.

In sexual reproduction, DNA of two gametes is combined, resulting in genetically-diverse offspring. Offspring of asexual reproduction are identical to their parents.

Drag each tile to the correct location. Match the characteristics to the correct phylum.

The characteristics of Phylum Cnidaria include the following:  

The characteristics of Phylum Porifera include the following:  

Taxonomy can be defined as a scientific method which is used by researchers for the naming, classification and description of the various species of organisms such as humans, animals, and plants.

In Science, the classification categories of organisms include the following:

For this exercise, you're required to match the characteristics of organisms to the correct Phylum as follows;

Read more on taxonomy here: brainly.com/question/1304906

What is a likely result of crossing over during meiosis I? Answer: CORRECT (SELECTED) Production of recombinant gametes, Explanation: During crossing over, homologous chromosomes trade parts, ultimately resulting in recombinant gametes at the end of meiosis.

We can confirm that the result of crossing over during meiosis I gives rise to new combinations of genes in the gametes at the end of meiosis .

It is the process of cell division that occurs in the gonads for the production of gametes that gives four haploid cells thus generates genetic diversity. During the crossing over , what will happen is that the homologous chromosomes exchange parts and new combinations are given for the gametes .

Therefore, we can confirm that the result of crossing over during meiosis I gives rise to new combinations of genes in the gametes at the end of meiosis .

To learn more about meiosis visit: https://brainly.com/question/11622266?referrer=searchResults

Listen Which components of a reading assignment can be scanned as part of a before-reading strategy? O questions and answers O explanations and mathematical equations O glossary and index O graphs and symbols

Which of the following answer choices best describes the image shown above? CORRECT (SELECTED) It depicts asexual reproduction occurring through binary fission, a process that does not lead to genetic variation. This organism lacks a nucleus and membrane-bound organelles, so it must be a prokaryote. Prokaryotes reproduce through asexual reproduction, specifically binary fission, resulting in two identical daughter cells.

The image depicts asexual reproduction taking place through binary fission , a process that does not lead to genetic variation . This organism lacks a nucleus and membrane-bound organelles, so it must be a prokaryote. Prokaryotes reproduce through asexual reproduction, specifically binary fission, resulting in two identical daughter cells.

Binary division, a process also called fission and cissiparity, is a type of asexual reproduction in which organisms equal to the one that generated them are formed. Reproduction i s defined in Biology as the way in which living organisms produce offspring.

prokaryotes. The name of this cell comes from the Greek “pro” (before, first) and “karyon” (nucleus), that is, “before the nucleus” . Prokaryotic cells are formed by

with the absence of the karyotheca that divides the c ell nucleus .

With this information, we can conclude that Binary division , a process also called fission and cissiparit y, is a type of asexual reproduction in which organisms equal to the one that generated them are formed.

Learn more about Binary division in brainly.com/question/11013499

Understanding that our understanding might be wrong is essential, and trying to figure out the ways we may be mistaken is the only way that science can help us find our way to the truth. What is meant by this statement? Explain.

Calcium oxide (CaO) forms when an atom of calcium loses two electrons, giving it a +2 charge and an atom of oxygen picks up two electrons, giving it a –2 charge. What is calcium oxide? A. an element B. a covalently bonded compound C. an ionically bonded compound

I am not sure if it correct it is what a shot I think the answer is C . an ionically bonded compound

Analyze the Pedegree shown. The trait in the Pedegree above is blank and blank

The Pedigree diagram is not found here but it can be examined by following the lineage of a given trait, which is normally evidenced by colored circles (woman) or squares (men).

A pedigree is a diagram used in genetics-related fields to show the inheritance pattern of a particular trait and/or genetic condition.

In a pedigree , squares represent males , while circles represent females , and colored representations are used to evidence the expression of the target phenotypic trait.

Moreover, the number of successive generations in the pedigree is denoted as I, II, III, etc. (from parental to offspring linages).

In conclusion, the Pedigree diagram is not found here but it can be examined by following the lineage of a given trait, which is normally evidenced by colored circles (woman) or squares (men).

Learn more about pedigree here:

https://brainly.com/question/15589113

Which of the following is true of binary fission? Answer: CORRECT (SELECTED) It produces two identical daughter cells. Explanation: Binary fission is asexual, so the offspring are identical to the parent cell.

Binary fission produces two identical daughter cells, it is asexual, so the offspring are identical to the parent cell.

Binary division , a process also called fission and cissiparity , is a type of asexual reproduction in which organisms equal to the one that generated them are formed. Reproduction is defined in Biology as the way in which living organisms produce offspring.

Bacterial binary fission is similar in some respects to mitosis that takes place in humans and other eukaryotes. In either case, the chromosomes are copied and separated, and the cell divides its cytoplasm to form two new cells.

With this information, we can conclude that Binary division, a process also called fission and cissiparity, is a type of asexual reproduction in which organisms equal to the one that generated them are formed.

Learn more about Binary division in brainly.com/question/7639952

The pedigree below tracks the presence of attached earlobes through a family's generations. Having attached earlobes is an autosomal recessive trait. If individual III-6 married a man who was homozygous for unattached earlobes, what is most likely to be true regarding their children? Answer: CORRECT (SELECTED) All of their children would have unattached earlobes. Explanation Individual III-6 has attached earlobes (ee). If she has children with a homozygous dominant man (EE), all of her children will be heterozygous and have unattached earlobes.

A pedigree is a representation of a family history tracking a trait and  showing the inheritance pattern of the trait and its expression . The whole progeny expresses unattached earlobes.

The autosomal recessive trait is the characteristic that is coded by a gene located in an autosomal chromosome (this is, not a sex chromosome ).

This trait is recessive because it is coded by the recessive allele , meaning that the dominant allele hides its expression .

The presence of only one dominant allele in the genotype is enough for the idividual to express free earlobes.

             Genotype                                  Phenotype    

EE, Homozygous dominant                  Free earlobes

Ee, Heterozygous                                  Free earlobes

ee, Homozygous recessive                   Attached earlobes

The pedigree is the representation of a family history conserning a certain trait. In this case, attached earlobes.

The pedigree shows the expression -and inheritance pattern - of the trait through several generations .

To correctly interpret a pedigree, we need to know that

Family members

→ Individuals are represented with geometrical figures.

→ Males are squares

→ Females are circles

Trait/Phenotype

→ Healthy/normal/not affected  individuals are represented with empty figures

→ Affected/mutated individuals are represented with solid black figures

Generations

→ Each file is represented with a roman number, indicating the Generation.

In the exposed example, we will assume that

According to this pedigree,

I- 1- man with attached earlobes (shaded)

I- 2- woman with unattached earlobes (empty)

II-1 - man with attached earlobes (shaded)

II-2 -woman with unattached earlobes (empty)

II-3 - man with attached earlobes (shaded)

II-4- do not have information

II-5 - woman with unattached earlobes (empty)

III-1 - man with attached earlobes (shaded)

III-2 - woman with attached earlobes (shaded)

III-3 - man with attached earlobes (shaded)

III-4 - no information

III-5 - No information

III-6 - woman with unattached earlobes (empty)

Since we so not have enough information, we will assume

Individual III-6 is a woman that expresses unattached earlobes. Since we do not know her genotype ( homozygous or heterozygous ), we will represent it as E- ⇒ The symbol - represents either the dominant or recessive allele .

This woman marries a man who was homozygous for unattached earlobes, EE .

Parentals)  E-    x     EE

Gametes ) E   -     E     E

Punnett square )  E       -

                    E     EE     E-

F1) 50% homozygous dominant EE

     50% is expected to be either homozygous dominant EE or heterozygous Ee

     100% is expected to have unattached earlobes.

Their whole progeny is expected to express unatached earlobes, because the father (EE) can only provide dominant alleles , and the simple presente of a dominant allele is enought to express the dominant trait.

IMPORTANT NOTE:

Up to this point we consider shaded shapes as individuals carring the trait attached earlobes. So, according to this reasoning, all shaded individuals must express the recessive trait and genotype ee.

However, according to the provided explanation, shaded individuals express the dominant trait.

If this is the case, individual III-6 is homozygous recessive ee expressing attached earlobes.

When she marries a homozygous dominant man expressing unattached earlobes EE, their children will only be heterozygous Ee and express unattached earlobes.

You will find both options in the attaced files. In any case, the whole progeny expresses unattached earlobes.

You can learn more about pedigrees at

brainly.com/question/19516649

all of their children would have unattached earlobes

What is the most reliable, accurate method for inferring phylogenetic relationships?direct observations genetic sequencing tested hypotheses fossil evidence

The method most reliable for inferring phylogenetic relationships is genetic sequencing .

They are processes that allow knowing and deciphering the genetic code that living beings have, this informs the kind of genetic information that is carried in a certain segment of DNA .

In phylogenetics it is used to investigate the reactions that exist in living beings through these sequences of DNA , RNA and proteins . The similarity in the sequence of nucleotides is interpreted as synapomorphies , also the similarities can be ambiguous since in the same sequence it can be related to two or more organisms , which gives alternatives regarding the kinship.

Therefore, we can confirm that the method most reliable for inferring phylogenetic relationships is genetic sequencing .

To learn more about phylogenetic visit: https://brainly.com/question/13577065?referrer=searchResults

Question 6 (2.5 points) Which of the following is an example of a reasoned and persuasive argument regarding an ethical issue? a) Evading taxes is immoral because the government uses taxes to fund social welfare programs. b) Walking barefoot is morally incorrect because feet tend to have an unpleasant odor. c) Those that send SPAM email are morally wrong, because the email recipient doesn't enjoy reading unsolicited advertisements. d) Excessive sun exposure is unethical because it prematurely ages the appearance of skin.

The best example of an ethical issue is the statement: ' Evading taxes is immoral because the government uses taxes to fund social welfare programs' option A.

Ethical issues are issues which are concerned with proper and acceptable behavior guiding an establishment or a group of people in a society or an organization.

Ethical issues are very prominently debated in the society.

An example of an ethical issue is the paying of taxes or not.

Since the paying of taxes enables the government to carry out various social and welfare projects that citizens enjoy, it is therefore expected of citizens to pay their taxes.

Evading taxes is thus a serious ethical issue.

Based on the proper reasoning and persuasion, the best example of an ethical issue is: Evading taxes is immoral because the government uses taxes to fund social welfare programs' option A.

In conclusion, ethical issues are issues which affect proper behavior and ethics.

Learn more about ethical issues at: https://brainly.com/question/9497017

As part of a class project, Claire needs to draw a food chain of the animals she sees in a zoo. Where in the food chain should she place rabbits?

Answer: She should place rabbits as a primary consumer in the food chain.

Explanation: Rabbits are herbivores (an animal that only feeds on plants). Therefore rabbits are primary consumers.

Type of proteins made by free ribosomes

Question 4 of 10 Which organisms would have the most similar traits? A. Organisms in the same kingdom B. Organisms in the same phylum C. Organisms in the same class OD. Organisms in the same order SUBMIT

C Organisms in the Same Order.

By biological classification Organisms in the same order come in as the closest currently listed.

Which of the following occurs to the zygote immediately after fertilization? Answer: CORRECT (SELECTED) The zygote divides, ultimately forming a hollow bundle of cells. Explanation: Once a zygote forms, it begins to rapidly divide. It first forms a solid ball of cells before forming a hollow bundle of cells called a blastocyst.

After fertilization , the zygote immediately undergoes a rapid stage of cleavage and division to form the 32-celled stage known as a blastula .

Fertilization may be defined as the process through which the respective gametes of a male partner (sperm) and female partner (ovum) are successfully fused to form a zygote .

This 32-celled stage comprises of hollow space of cells which is known as blastocysts . These blastocyst cells further undergo a process called gastrulation , in which the speciation of three germ layers forms, i.e. ectoderm, mesoderm, and endoderm.

Therefore, after fertilization , the zygote immediately undergoes a rapid stage of cleavage and division to form the 32-celled stage known as a blastula .

To learn more about Processes after fertilization , refer to the link:

https://brainly.com/question/4335996

To compare the temperature and precipitation data for San Francisco, what type of graph is most suitable? Construct a graph (with ALL the required components). Title, independent and dependent variables, scales for each variable, legend Please ignore the numbers on the side. Hurry please!​

To compare the temperature and precipitation data for San Francisco, the  type of graph is most suitable is   single graph ( Climographs ).

The Climographs is known to be a kind of a graph that is often made up of the monthly average temperatures as well as the precipitation totals that is known to be placed on a single graph.

In a single graph , the  graph is one that is not made up or do  not have more than one edge that is seen between any two vertices.

Hence, To compare the temperature and precipitation data for San Francisco, the  type of graph is most suitable is   single graph ( Climographs ).

Learn more about   graph from

https://brainly.com/question/25799000

What concept did Abu Ali al Hasa help develop

The concept Abu Ali al Hasa help develop is the comprehensive theory of ray of light.

Abu Ali Al Hasa was a mathematician, astrologist, engineer, physicist who developed the concept of comprehensive ray theory of light which gave him the opportunity to formulate one on one correspondence between every point of the eyes and the points in the visual field.. He was considered as the father of optics and he also invented a camera.

Therefore, The concept Abu Ali al Hasa help develop is the comprehensive theory of ray of light.

Learn more about theory of ray of light below.

https://brainly.com/question/2242368

11. What characteristic must be true of a good hypothesis? A. It must be correct. B. It must have been observed many times. C. It must involve quantitative data. D. It must be testable by observation or experiment.

The characteristic that must be true of a good hypothesis is that it must be testable by observation or experiment (option D).

Hypothesis is a tentative conjecture explaining an observation, phenomenon or scientific problem that can be tested by further observation, investigation and/or experimentation .

Hypothesis is a significant part of the scientific method that must be included in any scientific procedure.

The hypothesis must be falsifiable or testable using an experiment or observation.

The hypothesis is widely known as an educated guess because it predicts the possible outcome of an experiment.

Therefore, the characteristic that must be true of a good hypothesis is that it must be testable by observation or experiment .

Learn more about hypothesis at: https://brainly.com/question/13025783

What stage of mitosis is pictured? Answer: CORRECT (SELECTED) Metaphase. Explanatioin: The chromosomes in this cell are aligned at the metaphase plate, so this cell is in metaphase.

The stage of mitosis pictured in the above image is metaphase .

Mitosis is the division of a cell nucleus in which the genome is copied and separated into two identical halves. It is normally followed by cell division .

Mitotic process is employed by living cells for growth and development. The process of mitosis is made up of the following stages:

Prophase is the first stage of mitosis, during which chromatin condenses to form the chromosomes .

Metaphase is the stage of mitosis during which condensed chromosomes become aligned before being separated.

According to the above image, chromosomes can be observed to be aligned at the centre of the cell, hence, the stage of mitosis pictured in the above image is metaphase .

Learn more about metaphase at: https://brainly.com/question/16992029

Which explanation explains why cells use glucose/blood sugar as their primary energy source? (awnsers) Because glucose is a complex carbohydrate which makes it easier to be broken down by the cell. Because glucose is less complex than other molecules, thus making it easier to be broken down for cellular respiration. Because glucose is more complex than other molecules, thus making it harder to be broken down for cellular respiration.

The explanation that tells better why cells use glucose /blood sugar as their primary energy source is: because glucose is less complex than other molecules , thus making it easier to be broken down for cellular respiration .

Glucose is one of the simplest carbohydrates , It is a monosaccharide . It is the body's most important and preferred source of energy , found in the foods we usually eat as bread, fruits, vegetables and dairy products. An adequate level of glucose in the blood is necessary for the proper functioning of the body since some organs cannot use other forms of energy.

If blood glucose drops, the brain will receive an inadequate supply of glucose and this will affect brain function and can lead to coma and death . High blood glucose accelerates non-enzymatic glycosylation of proteins , one of the processes related to protein aging. This can modify the catalytic activity of enzymes .

Therefore, we can confirm that cells use glucose as their primary energy source because is less complex than other molecules , thus making it easier to be broken down for cellular respiration .

To learn more about glucose visit: https://brainly.com/question/2396657?referrer=searchResults

What event is labeled as Process Z in the diagram? Answer: CORRECT (SELECTED) Meiosis II. Explanation: During process Z, sister chromatids are separating to form four haploid gametes. This occurs during meiosis II.

The process of the formation of the gametes from the germinal cell is due to meiosis cell division. The event labeled process Z in the diagram shows meiosis II.

Meiosis II is the cell division process where four daughter cells are produced that have half the ploidy as the parent cell. This type of division has been seen in the reproductive cells of the organisms.

The process labeled Z shows the separation of the homologous chromosomes to produce the sister chromatids that separate to produce four cells with haploidy.

It is an equational division where the germinal cells produce the gametes essential for reproduction and fertilization . It shows recombination is crucial for genetic variability .

Therefore, process label Z is meiosis II.

Learn more about meiosis II here:

https://brainly.com/question/9650142

How does the Phosphorus Cycle differ from the other biogeochemical cycles? A. Phosphorus is not found as a mineral, only in organic form. B. Phosphorus only cycles through the environment, not through organisms. C. Phosphorus doesn’t enter the water. D. Phosphorus doesn’t enter the atmosphere directly.

The phosphorus Cycle differ from the other biogeochemical cycles in the sense that phosphorus doesn’t enter the atmosphere directly. Option D

The biogeochemical cycles refer to the movement of elements and other essential materials in nature. We know that these materials are required by living things for the smooth functioning of the systems in the body hence these cycles are very vital.

The phosphorus Cycle differ from the other biogeochemical cycles in the sense that phosphorus doesn’t enter the atmosphere directly.

Learn more about phosphorus cycle :https://brainly.com/question/15020567

1.3.1 A form of security required by the bank before granting a loan 1.3.2 A financial statement that summarises the assets and liabilities of the business 1.3.3 A document that provides an estimate of expected income and expenditure for given period 1.3.4 The combination of product, pricing, placement and promotion 1.3.5 The sequence of steps involved in transferring produce from the farm to the consumer​

1.3.1 A form of security required by the bank before granting a loan is called collateral .

1.3.2 A financial statement that summarises the assets and liabilities of the business is called a balance sheet.

1.3.3 A document that provides an estimate of expected income and expenditure for a given period is an income statement.

1.3.4 The combination of product, pricing, placement, and promotion is called the marketing mix.

1.3.5 The sequence of steps involved in transferring products from the farm to the consumer​ Direct marketing .

An asset that a lender accepts as collateral for a loan is referred to as collatera l. Depending on the loan's purpose, collateral may be in the form of real estate or other forms of assets. For the lender, the collateral serves as a type of insurance.

The act of presenting an offer directly to a target client and providing them with a way to respond immediately is known as direct marketing . It is sometimes referred to as direct response marketing among practitioners. Advertising, in contrast, is a form of mass messaging.

To know more about collateral refer to:  https://brainly.com/question/6779619

Gregor Mendel is famous for his experiments on Pisum sativum (pea plant, pictured above), which is capable of self-fertilizing by combining its own male and female gametes. What type of reproduction does Pisum sativum undergo and why? Answer: CORRECT (SELECTED) Sexual reproduction; sperm and egg will fuse to form a unique zygote. Explanation: Pisum sativum is a hermaphrodite, meaning that it has both male and female sex organs, each producing sperm and egg. Once they fuse during self-fertilization, the result will be a unique plant offspring.

The type of reproduction that Pisum sativum undergoes is sexual reproduction because it involves the fusion of male and female gametes.

Sexual reproduction is a type of reproduction that involves the formation of a new organism by combining the genetic material of two organisms.

The genetic material of each parent organism is located in the gametes (sperm and egg) produced during the process of meiosis.

According to this question, Gregor Mendel is said to be famous for his experiments on Pisum sativum (pea plant) which is capable of self-fertilizing by combining its own male and female gametes .

This type of reproduction that Pisum sativum undergoes is sexual reproduction because it involves the fusion of male and female gametes .

Learn more about sexual reproduction at: https://brainly.com/question/7464705

Question: Some species of bacteria can live in hot springs. Their cells contain enzymes that function best at temperatures of 70∘ C or higher. How will the enzymes in these bacteria most likely change if the temperature is lowered to 45∘ C? Answer: CORRECT (SELECTED) The enzymes will not catalyze reactions as efficiently. Explanation: 70∘ C is the optimal temperature for the bacteria's enzymes to function. A lower temperature would reduce the enzyme's efficiency in speeding up a reaction.

The efficiency of the enzyme would be highly reduced if the temperature of the surrounding is reduced to about 45∘ C.

Enzymes are those substances that are found to increase the rate of a biological reaction. These substances are proteins are always produced in the body. The are specific in their action so no enzyme can catalyze two reactions. This is because the enzymes are able to interact with their substrates in a  lock and key model.

Now, given the fact that the enzymes that function best at temperatures of 70∘ C or higher , the efficiency of the enzyme would be highly reduced if the temperature of the surrounding is reduced to about 45∘ C.

Learn more about enzymes :https://brainly.com/question/14953274

what are the best animal thats an organism ??

• Download PDF
• Share X Facebook Email LinkedIn
• Permissions

Why Stating Hypotheses in Grant Applications Is Unnecessary

• 1 CAUSALab, Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
• 2 Department of Epidemiology and Department of Statistics, University of California, Los Angeles
• Original Investigation Use of Promotional Language in Abstracts of Successful NIH Grant Applications Neil Millar, PhD; Bojan Batalo, MSc; Brian Budgell, PhD JAMA Network Open

“Our hypothesis is that statins do not increase the risk of cancer.” Such explicit statement of investigators’ beliefs is often found in applications for research funding, which follows common advice on how to write grant applications by colleagues, 1 - 3  academic institutions , and funding  agencies . The statement of the hypothesis is viewed as “the backbone of your grant.” 1 Hence, many investigators, aware that hypothesis-driven research is highly regarded by funders and reviewers, declare their hypothesis in their grant applications. This hypothesis-centric approach, however, is problematic, as the following example of causal inference from observational data illustrates.

Two questions for the authors and for readers:

1. Might the hypothesis statement, as described in the article and as commonly used in current practice, still provide some benefit by forcing an early insight into a potential direction of the investigators’ bias? 2. Might a more beneficial way to structure the hypothesis include a summary of proposed causal underpinnings?

Read More About

Hernán MA , Greenland S. Why Stating Hypotheses in Grant Applications Is Unnecessary. JAMA. 2024;331(4):285–286. doi:10.1001/jama.2023.27163

Manage citations:

© 2024

Artificial Intelligence Resource Center

Cardiology in JAMA : Read the Latest

Browse and subscribe to JAMA Network podcasts!

Others Also Liked

Select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing
• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Do human assertions really adhere strictly to norms the effect of threatening content in information on personalized norm perception.

1. Introduction

1.1. the norm of assertion and its research progress, 1.2. justified-belief norm facing shocks and potential hypotheses, 1.3. the potential value and significance of testing the above hypotheses, 1.4. research program, 2. experiment 1, 2.1. participants, materials, and procedure.

• General & Justify Belief—Jack is waiting for a flight to Russia at New York’s Kennedy International Airport. An elderly woman asks him if he can tell her which boarding gate the flight to France departs from. Jack remembers seeing a list of boarding gate information where the only gate listed for flights to France was Gate 24.
• Threatening & Justify Belief—Jack is waiting for a flight to Russia at New York’s Kennedy International Airport. An elderly woman asks him if she can board a flight to France through Gate 24. Jack recalls seeing a notice stating that Gate 24 has serious structural issues and could collapse, potentially harming those passing through.
• General & Belief—Jack is waiting for a flight to Russia at New York’s Kennedy International Airport. An elderly woman asks him if he can tell her which boarding gate the flight to France departs from. Jack couldn’t find any information regarding boarding gates, but he has a feeling that the flight to France leaves from Gate 24, so he believes it might be there.
• Threatening & Belief—Jack is waiting for a flight to Russia at New York’s Kennedy International Airport. An elderly woman asks him if she can board a flight to France through Gate 24. Jack didn’t see any notice, but suddenly has a premonition that there might be a risk of the building collapsing as the woman passes through Gate 24, so he believes she might be in danger.
• Assertion Question 1—Do you think Jack can say, “The flight to France is at Gate 24”? (Yes/No)
• Assertion Question 2—Do you think Jack should say, “The flight to France is at Gate 24”? (Yes/No)
• Truth Question—Is the boarding gate for the flight to France really at Gate 24? (Yes/No)
• Belief Question—Does Jack believe that the boarding gate for the flight to France is at Gate 24? (Yes/No)
• Justify Question—Does Jack’s belief that “the flight to France is at Gate 24” have reasonable evidence? (Yes/No)
• Assertion Question 1—Do you think Jack can say, “There’s danger at Gate 24”? (Yes/No)
• Assertion Question 2—Do you think Jack should say, “There’s danger at Gate 24”? (Yes/No)
• Truth Question—Is there really a possibility of danger at Gate 24? (Yes/No)
• Belief Question—Does Jack believe that the elderly woman might encounter danger passing through Gate 24? (Yes/No)
• Justify Question—Does Jack’s belief that “the elderly woman might encounter danger” have good evidence? (Yes/No)

2.2. Results

2.3. discussion, 3. experiment 2, 3.1. participants, materials, and procedure.

• General Condition & Belief—Mary stands at the entrance of an ophthalmology hospital. A young person asks her if this hospital performs the LINU surgery to improve eyesight. Mary responds, “This hospital doesn’t perform the LINU surgery”. It’s important to note that Mary isn’t certain if the hospital does perform this surgery. She heard one claim stating the hospital lacks doctors skilled in this technique, but later heard another claim that the hospital does possess this technology. Nevertheless, Mary firmly believes the hospital doesn’t possess the LINU technology.
• Threatening Condition & Belief—Mary stands at the entrance of an ophthalmology hospital. A young person asks her if the LINU technique for improving eyesight is safe. Mary responds, “The LINU technique isn’t safe”. It’s important to note that Mary isn’t sure about the safety of the LINU technique. She heard one claim stating the LINU technique isn’t safe and might lead to deteriorating eyesight after a dozen years. However, she later heard another claim stating LINU is safe. Overall, Mary firmly believes LINU is an unsafe technique.
• General Condition & No Belief (hesitation)—Mary stands at the entrance of an ophthalmology hospital. A young person asks her if this hospital performs the LINU surgery to improve eyesight. Mary responds, “This hospital doesn’t perform the LINU surgery”. It’s important to note that Mary isn’t certain if the hospital performs this surgery. She heard one claim stating the hospital lacks doctors skilled in this technique, but later heard another claim that the hospital does possess this technology. In conclusion, Mary feels uncertain about the hospital’s possession of the LINU technology, her belief vacillates.
• Threatening Condition & No Belief (hesitation)—Mary stands at the entrance of an ophthalmology hospital. A young person asks her if the LINU technique for improving eyesight is safe. Mary responds, “The LINU technique isn’t safe”. It’s important to note that Mary isn’t sure about the safety of the LINU technique. She heard one claim stating the LINU technique isn’t safe and might lead to deteriorating eyesight after a dozen years. However, she later heard another claim stating LINU is safe. In conclusion, Mary feels uncertain about the safety of the LINU technique, her belief vacillates.
• Assertion Question 1: Should Mary say “This hospital cannot perform the LINU surgery”? (Yes/No)
• Assertion Question 2: Is Mary permitted to say “This hospital cannot perform the LINU surgery”? (Yes/No)
• Truth Question: Can this hospital really not perform the LINU surgery? (Yes/Uncertain/No)
• Belief Question: Does Mary believe “This hospital cannot perform the LINU surgery”? (Yes/Uncertain/No)
• Blame Question: Do you think Mary’s behavior should be blamed? (Yes/No)
• Assertion Question 1: Should Mary say “LINU surgery is unsafe”? (Yes/No)
• Assertion Question 2: Is Mary permitted to say “LINU surgery is unsafe”? (Yes/No)
• Truth Question: Is LINU surgery really unsafe? (Yes/Uncertain/No)
• Belief Question: Does Mary believe “LINU surgery is unsafe”? (Yes/Uncertain/No)

3.2. Results

3.3. discussion, 4. general discussion, 4.1. the effect of threatening content on individuals’ perceptions of assertions, 4.2. the effect of threatening content on “justified-belief norm”, 4.3. potential inspiration for other research questions arising, 4.4. research gaps and possible research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Green, M.S. Assertion ; Oxford University Press: Oxford, UK, 2017; Volume 1. [ Google Scholar ] [ CrossRef ]
• Marsili, N.; Green, M. Assertion: A (partly) social speech act. J. Pragmat. 2021 , 181 , 17–28. [ Google Scholar ] [ CrossRef ]
• Boult, C. Epistemic normativity and the justification-excuse distinction. Synthese 2017 , 194 , 4065–4081. [ Google Scholar ] [ CrossRef ]
• Reynolds, S.L. Testimony, Knowledge, and Epistemic Goals. Philos. Stud. 2002 , 110 , 139–161. [ Google Scholar ] [ CrossRef ]
• Turri, J. The test of truth: An experimental investigation of the norm of assertion. Cognition 2013 , 129 , 279–291. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Turri, J. Revisiting norms of assertion. Cognition 2018 , 177 , 8–11. [ Google Scholar ] [ CrossRef ]
• Kneer, M. The norm of assertion: Empirical data. Cognition 2018 , 177 , 165–171. [ Google Scholar ] [ CrossRef ]
• Benton, M.A. Expert Opinion and Second-Hand Knowledge. Philos. Phenomenol. Res. 2016 , 92 , 492–508. [ Google Scholar ] [ CrossRef ]
• Blaauw, M.J. Reinforcing the knowledge account of assertion. Analysis 2012 , 72 , 105–108. [ Google Scholar ] [ CrossRef ]
• Brogaard, B. Intellectual Flourishing as the Fundamental Epistemic Norm. In Epistemic Norms ; Littlejohn, C., Turri, J., Eds.; Oxford University Press: Oxford, UK, 2014; pp. 11–31. [ Google Scholar ] [ CrossRef ]
• Schaffer, J. Knowledge in the Image of Assertion. Philos. Issues 2008 , 18 , 1–19. [ Google Scholar ] [ CrossRef ]
• Turri, J. The Express Knowledge Account of Assertion. Australas. J. Philos. 2011 , 89 , 37–45. [ Google Scholar ] [ CrossRef ]
• Williamson, T. Knowing and Asserting. Philos. Rev. 1996 , 105 , 489–523. [ Google Scholar ] [ CrossRef ]
• Williamson, T. Knowledge and its Limits ; Oxford University Press: Oxford, UK, 2000. [ Google Scholar ]
• Weiner, M. Must We Know What We Say? Philos. Rev. 2005 , 114 , 227–251. [ Google Scholar ] [ CrossRef ]
• Bach, K. Applying pragmatics to epistemology. Philos. Issues 2008 , 18 , 68–88. [ Google Scholar ] [ CrossRef ]
• Bach, K.; Harnish, R. Linguistic Communication and Speech Acts ; MIT Press: Cambridge, MA, USA, 1979. [ Google Scholar ]
• Douven, I. Assertion, Knowledge, and Rational Credibility. Philos. Rev. 2006 , 115 , 449–485. [ Google Scholar ] [ CrossRef ]
• Lackey, J. Norms of Assertion. Noûs 2007 , 41 , 594–626. [ Google Scholar ] [ CrossRef ]
• Kneer, M. Norms of assertion in the United States, Germany, and Japan. Proc. Natl. Acad. Sci. USA 2021 , 118 , e2105365118. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Marsili, N.; Wiegmann, A. Should I say that? An experimental investigation of the norm of assertion. Cognition 2021 , 212 , 104657. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jungherr, A.; Schroeder, R. Disinformation and the Structural Transformations of the Public Arena: Addressing the Actual Challenges to Democracy. Soc. Media Soc. 2021 , 7 , 2056305121988928. [ Google Scholar ] [ CrossRef ]
• Kappes, A.; Harvey, A.H.; Lohrenz, T.; Montague, P.R.; Sharot, T. Confirmation bias in the utilization of others’ opinion strength. Nat. Neurosci. 2020 , 23 , 130–137. [ Google Scholar ] [ CrossRef ]
• Tomaszewski, T.; Morales, A.; Lourentzou, I.; Caskey, R.; Liu, B.; Schwartz, A.; Chin, J. Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions from Twitter: Advanced Predictive Models. J. Med. Internet Res. 2021 , 23 , e30451. [ Google Scholar ] [ CrossRef ]
• Bao, L.; Peng, C.; He, J.; Sun, C.; Feng, L.; Luo, Y. The Relationship between Fear Avoidance Belief and Threat Learning in Postoperative Patients After Lung Surgery: An Observational Study. Psychol. Res. Behav. Manag. 2023 , 16 , 3259–3267. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, S.; Zheng, J.; Mo, L. The effect of the brightness metaphor on memory. Psychol. Res. Psychol. Forsch. 2022 , 86 , 1751–1762. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Puttlitz, M.H.; Chivers, D.P.; Kiesecker, J.M.; Blaustein, A.R. Threat-sensitive predator avoidance by larval pacific treefrogs (Amphibia, Hylidae). Ethology 1999 , 105 , 449–456. [ Google Scholar ] [ CrossRef ]
• Rieucau, G.; Boswell, K.M.; Robertis, A.D.; Macaulay, G.J.; Handegard, N.O. Experimental Evidence of Threat-Sensitive Collective Avoidance Responses in a Large Wild-Caught Herring School. PLoS ONE 2014 , 9 , e86726. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Betz-Richman, N. Lying, hedging, and the norms of assertion. Synthese 2022 , 200 , 176. [ Google Scholar ] [ CrossRef ]
• Ceccarini, F.; Capuozzo, P.; Colpizzi, I.; Caudek, C. Breaking (Fake) News: No Personal Relevance Effect on Misinformation Vulnerability. Behav. Sci. 2023 , 13 , 11. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fabian, M.; Pykett, J. Be Happy: Navigating Normative Issues in Behavioral and Well-Being Public Policy. Perspect. Psychol. Sci. 2022 , 17 , 169–182. [ Google Scholar ] [ CrossRef ]
• Enders, A.M.; Uscinski, J.E.; Klofstad, C.A.; Seelig, M.I.; Wuchty, S.; Murthi, M.N.; Premaratne, K.; Funchion, J.R. Do Conspiracy Beliefs Form a Belief System? Examining the Structure and Organization of Conspiracy Beliefs. J. Soc. Political Psychol. 2021 , 9 , 255–271. [ Google Scholar ] [ CrossRef ]
• Wang, C.; Zhou, R.; Zhang, X. Positive Childhood Experiences and Depression Among College Students During the COVID-19 Pandemic: A Moderated Mediation Model. Psychol. Res. Behav. Manag. 2023 , 16 , 4105–4115. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Zhang, S.; Diao, J.; Huang, J.; Liu, Y.; Mo, L. Do Human Assertions Really Adhere Strictly to Norms? The Effect of Threatening Content in Information on Personalized Norm Perception. Behav. Sci. 2024 , 14 , 625. https://doi.org/10.3390/bs14070625

Zhang S, Diao J, Huang J, Liu Y, Mo L. Do Human Assertions Really Adhere Strictly to Norms? The Effect of Threatening Content in Information on Personalized Norm Perception. Behavioral Sciences . 2024; 14(7):625. https://doi.org/10.3390/bs14070625

Zhang, Shijia, Jiangdong Diao, Jiahui Huang, Yanchi Liu, and Lei Mo. 2024. "Do Human Assertions Really Adhere Strictly to Norms? The Effect of Threatening Content in Information on Personalized Norm Perception" Behavioral Sciences 14, no. 7: 625. https://doi.org/10.3390/bs14070625

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals