how to state a null hypothesis in a research paper

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How to Write a Null Hypothesis (5 Examples)

A hypothesis test uses sample data to determine whether or not some claim about a population parameter is true.

Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms:

H 0 (Null Hypothesis): Population parameter =, ≤, ≥ some value

H A (Alternative Hypothesis): Population parameter <, >, ≠ some value

Note that the null hypothesis always contains the equal sign .

We interpret the hypotheses as follows:

Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.

Alternative hypothesis: The sample data does provide sufficient evidence to support the claim being made by an individual.

For example, suppose it’s assumed that the average height of a certain species of plant is 20 inches tall. However, one botanist claims the true average height is greater than 20 inches.

To test this claim, she may go out and collect a random sample of plants. She can then use this sample data to perform a hypothesis test using the following two hypotheses:

H 0 : μ ≤ 20 (the true mean height of plants is equal to or even less than 20 inches)

H A : μ > 20 (the true mean height of plants is greater than 20 inches)

If the sample data gathered by the botanist shows that the mean height of this species of plants is significantly greater than 20 inches, she can reject the null hypothesis and conclude that the mean height is greater than 20 inches.

Read through the following examples to gain a better understanding of how to write a null hypothesis in different situations.

Example 1: Weight of Turtles

A biologist wants to test whether or not the true mean weight of a certain species of turtles is 300 pounds. To test this, he goes out and measures the weight of a random sample of 40 turtles.

Here is how to write the null and alternative hypotheses for this scenario:

H 0 : μ = 300 (the true mean weight is equal to 300 pounds)

H A : μ ≠ 300 (the true mean weight is not equal to 300 pounds)

Example 2: Height of Males

It’s assumed that the mean height of males in a certain city is 68 inches. However, an independent researcher believes the true mean height is greater than 68 inches. To test this, he goes out and collects the height of 50 males in the city.

H 0 : μ ≤ 68 (the true mean height is equal to or even less than 68 inches)

H A : μ > 68 (the true mean height is greater than 68 inches)

Example 3: Graduation Rates

A university states that 80% of all students graduate on time. However, an independent researcher believes that less than 80% of all students graduate on time. To test this, she collects data on the proportion of students who graduated on time last year at the university.

H 0 : p ≥ 0.80 (the true proportion of students who graduate on time is 80% or higher)

H A : μ < 0.80 (the true proportion of students who graduate on time is less than 80%)

Example 4: Burger Weights

A food researcher wants to test whether or not the true mean weight of a burger at a certain restaurant is 7 ounces. To test this, he goes out and measures the weight of a random sample of 20 burgers from this restaurant.

H 0 : μ = 7 (the true mean weight is equal to 7 ounces)

H A : μ ≠ 7 (the true mean weight is not equal to 7 ounces)

Example 5: Citizen Support

A politician claims that less than 30% of citizens in a certain town support a certain law. To test this, he goes out and surveys 200 citizens on whether or not they support the law.

H 0 : p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%)

H A : μ < 0.30 (the true proportion of citizens who support the law is less than 30%)

Additional Resources

Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance

Published by Zach

Writing Null Hypotheses in Research and Statistics

Last Updated: January 17, 2024 Fact Checked

This article was co-authored by Joseph Quinones and by wikiHow staff writer, Jennifer Mueller, JD . Joseph Quinones is a High School Physics Teacher working at South Bronx Community Charter High School. Joseph specializes in astronomy and astrophysics and is interested in science education and science outreach, currently practicing ways to make physics accessible to more students with the goal of bringing more students of color into the STEM fields. He has experience working on Astrophysics research projects at the Museum of Natural History (AMNH). Joseph recieved his Bachelor's degree in Physics from Lehman College and his Masters in Physics Education from City College of New York (CCNY). He is also a member of a network called New York City Men Teach. There are 7 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 23,702 times.

Are you working on a research project and struggling with how to write a null hypothesis? Well, you've come to the right place! Start by recognizing that the basic definition of "null" is "none" or "zero"—that's your biggest clue as to what a null hypothesis should say. Keep reading to learn everything you need to know about the null hypothesis, including how it relates to your research question and your alternative hypothesis as well as how to use it in different types of studies.

Things You Should Know

Write a research null hypothesis as a statement that the studied variables have no relationship to each other, or that there's no difference between 2 groups.

$\mu _{1}=\mu _{2}$

Adjust the format of your null hypothesis to match the statistical method you used to test it, such as using "mean" if you're comparing the mean between 2 groups.

What is a null hypothesis?

A null hypothesis states that there's no relationship between 2 variables.

Research hypothesis: States in plain language that there's no relationship between the 2 variables or there's no difference between the 2 groups being studied.
Statistical hypothesis: States the predicted outcome of statistical analysis through a mathematical equation related to the statistical method you're using.

Examples of Null Hypotheses

Null Hypothesis vs. Alternative Hypothesis

Step 1 Null hypotheses and alternative hypotheses are mutually exclusive.

For example, your alternative hypothesis could state a positive correlation between 2 variables while your null hypothesis states there's no relationship. If there's a negative correlation, then both hypotheses are false.

Step 2 Proving the null hypothesis false is a precursor to proving the alternative.

You need additional data or evidence to show that your alternative hypothesis is correct—proving the null hypothesis false is just the first step.
In smaller studies, sometimes it's enough to show that there's some relationship and your hypothesis could be correct—you can leave the additional proof as an open question for other researchers to tackle.

How do I test a null hypothesis?

Use statistical methods on collected data to test the null hypothesis.

Group means: Compare the mean of the variable in your sample with the mean of the variable in the general population. [6] X Research source
Group proportions: Compare the proportion of the variable in your sample with the proportion of the variable in the general population. [7] X Research source
Correlation: Correlation analysis looks at the relationship between 2 variables—specifically, whether they tend to happen together. [8] X Research source
Regression: Regression analysis reveals the correlation between 2 variables while also controlling for the effect of other, interrelated variables. [9] X Research source

Templates for Null Hypotheses

Research null hypothesis: There is no difference in the mean [dependent variable] between [group 1] and [group 2].

$\mu _{1}+\mu _{2}=0$

Research null hypothesis: The proportion of [dependent variable] in [group 1] and [group 2] is the same.

$p_{1}=p_{2}$

Research null hypothesis: There is no correlation between [independent variable] and [dependent variable] in the population.

$\rho =0$

Research null hypothesis: There is no relationship between [independent variable] and [dependent variable] in the population.

$\beta =0$

Expert Q&A

Expert Interview

Thanks for reading our article! If you’d like to learn more about physics, check out our in-depth interview with Joseph Quinones .

↑ https://online.stat.psu.edu/stat100/lesson/10/10.1
↑ https://online.stat.psu.edu/stat501/lesson/2/2.12
↑ https://support.minitab.com/en-us/minitab/21/help-and-how-to/statistics/basic-statistics/supporting-topics/basics/null-and-alternative-hypotheses/
↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635437/
↑ https://online.stat.psu.edu/statprogram/reviews/statistical-concepts/hypothesis-testing
↑ https://education.arcus.chop.edu/null-hypothesis-testing/
↑ https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_hypothesistest-means-proportions/bs704_hypothesistest-means-proportions_print.html

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Methodology

How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

An independent variable is something the researcher changes or controls.
A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

The relevant variables
The specific group being studied
The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

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.

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.

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Knowledge Base
Methodology
How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

The relevant variables
The specific group being studied
The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

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 Reference Generator.

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 9 April 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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Null Hypothesis

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Fisher, R. (1925). Statistical methods for research workers (1st ed.). Edinburgh: Oliver and Boyd.

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Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33 , 587–606.

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Hays, W. L. (1994). Statistics (5th ed.). Belmont: Wadsworth.

Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, 231 , 289–337.

Szucs, D., & Ioannidis, J. P. A. (2016). When null hypothesis significance testing is unsuitable for research: A reassessment. bioRxiv . https://doi.org/10.1101/095570 .

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7.3: The Research Hypothesis and the Null Hypothesis

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Michelle Oja
Taft College

Hypotheses are predictions of expected findings.

The Research Hypothesis

A research hypothesis is a mathematical way of stating a research question. A research hypothesis names the groups (we'll start with a sample and a population), what was measured, and which we think will have a higher mean. The last one gives the research hypothesis a direction. In other words, a research hypothesis should include:

The name of the groups being compared. This is sometimes considered the IV.
What was measured. This is the DV.
Which group are we predicting will have the higher mean.

There are two types of research hypotheses related to sample means and population means: Directional Research Hypotheses and Non-Directional Research Hypotheses

Directional Research Hypothesis

If we expect our obtained sample mean to be above or below the other group's mean (the population mean, for example), we have a directional hypothesis. There are two options:

Symbol: $ \displaystyle \bar{X} > \mu $
(The mean of the sample is greater than than the mean of the population.)
Symbol: $ \displaystyle \bar{X} < \mu $
(The mean of the sample is less than than mean of the population.)

Example $\PageIndex{1}$

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework. What’s a directional hypothesis for how scoring higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend more time on their homework than the population of junior high students.

Answer in Symbols: $ \displaystyle \bar{X} > \mu $

Non-Directional Research Hypothesis

A non-directional hypothesis states that the means will be different, but does not specify which will be higher. In reality, there is rarely a situation in which we actually don't want one group to be higher than the other, so we will focus on directional research hypotheses. There is only one option for a non-directional research hypothesis: "The sample mean differs from the population mean." These types of research hypotheses don’t give a direction, the hypothesis doesn’t say which will be higher or lower.

A non-directional research hypothesis in symbols should look like this: $ \displaystyle \bar{X} \neq \mu $ (The mean of the sample is not equal to the mean of the population).

Exercise $\PageIndex{1}$

What’s a non-directional hypothesis for how scoring higher on growth mindset higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework (Blackwell, Trzesniewski, & Dweck, 2007)? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend a different amount of time on their homework than the population of junior high students.

Answer in Symbols: $ \displaystyle \bar{X} \neq \mu $

See how a non-directional research hypothesis doesn't really make sense? The big issue is not if the two groups differ, but if one group seems to improve what was measured (if having a growth mindset leads to more time spent on math homework). This textbook will only use directional research hypotheses because researchers almost always have a predicted direction (meaning that we almost always know which group we think will score higher).

The Null Hypothesis

The hypothesis that an apparent effect is due to chance is called the null hypothesis, written $H_0$ (“H-naught”). We usually test this through comparing an experimental group to a comparison (control) group. This null hypothesis can be written as:

\[\mathrm{H}_{0}: \bar{X} = \mu \nonumber \]

For most of this textbook, the null hypothesis is that the means of the two groups are similar. Much later, the null hypothesis will be that there is no relationship between the two groups. Either way, remember that a null hypothesis is always saying that nothing is different.

This is where descriptive statistics diverge from inferential statistics. We know what the value of $\overline{\mathrm{X}}$ is – it’s not a mystery or a question, it is what we observed from the sample. What we are using inferential statistics to do is infer whether this sample's descriptive statistics probably represents the population's descriptive statistics. This is the null hypothesis, that the two groups are similar.

Keep in mind that the null hypothesis is typically the opposite of the research hypothesis. A research hypothesis for the ESP example is that those in my sample who say that they have ESP would get more correct answers than the population would get correct, while the null hypothesis is that the average number correct for the two groups will be similar.

In general, the null hypothesis is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. This is always our baseline starting assumption, and it is what we seek to reject. If we are trying to treat depression, we want to find a difference in average symptoms between our treatment and control groups. If we are trying to predict job performance, we want to find a relation between conscientiousness and evaluation scores. However, until we have evidence against it, we must use the null hypothesis as our starting point.

In sum, the null hypothesis is always : There is no difference between the groups’ means OR There is no relationship between the variables .

In the next chapter, the null hypothesis is that there’s no difference between the sample mean and population mean. In other words:

There is no mean difference between the sample and population.
The mean of the sample is the same as the mean of a specific population.
$\mathrm{H}_{0}: \bar{X} = \mu \nonumber $
We expect our sample’s mean to be same as the population mean.

Exercise $\PageIndex{2}$

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework. What’s the null hypothesis for scoring higher on growth mindset (compared to the population of junior high students) and how long students spent on their homework? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend a similar amount of time on their homework as the population of junior high students.

Answer in Symbols: $ \bar{X} = \mu $

Contributors and Attributions

Foster et al. (University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus)

Dr. MO ( Taft College )

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The Craft of Writing a Strong Hypothesis

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

A research hypothesis has to be simple yet clear to look justifiable enough.
It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1. Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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10.1 - setting the hypotheses: examples.

A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations or odds ratios or any other numerical summary of the population. The alternative hypothesis is typically the research hypothesis of interest. Here are some examples.

Example 10.2: Hypotheses with One Sample of One Categorical Variable Section

About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.

Research Question : Are artists more likely to be left-handed than people found in the general population?
Response Variable : Classification of the student as either right-handed or left-handed

State Null and Alternative Hypotheses

Null Hypothesis : Students in the College of Arts and Architecture are no more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture = 10% or p = .10).
Alternative Hypothesis : Students in the College of Arts and Architecture are more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Arts and Architecture > 10% or p > .10). This is a one-sided alternative hypothesis.

Example 10.3: Hypotheses with One Sample of One Measurement Variable Section

A generic brand of the anti-histamine Diphenhydramine markets a capsule with a 50 milligram dose. The manufacturer is worried that the machine that fills the capsules has come out of calibration and is no longer creating capsules with the appropriate dosage.

Research Question : Does the data suggest that the population mean dosage of this brand is different than 50 mg?
Response Variable : dosage of the active ingredient found by a chemical assay.
Null Hypothesis : On the average, the dosage sold under this brand is 50 mg (population mean dosage = 50 mg).
Alternative Hypothesis : On the average, the dosage sold under this brand is not 50 mg (population mean dosage ≠ 50 mg). This is a two-sided alternative hypothesis.

Example 10.4: Hypotheses with Two Samples of One Categorical Variable Section

Many people are starting to prefer vegetarian meals on a regular basis. Specifically, a researcher believes that females are more likely than males to eat vegetarian meals on a regular basis.

Research Question : Does the data suggest that females are more likely than males to eat vegetarian meals on a regular basis?
Response Variable : Classification of whether or not a person eats vegetarian meals on a regular basis
Explanatory (Grouping) Variable: Sex
Null Hypothesis : There is no sex effect regarding those who eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis = population percent of males who eat vegetarian meals on a regular basis or p females = p males ).
Alternative Hypothesis : Females are more likely than males to eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis > population percent of males who eat vegetarian meals on a regular basis or p females > p males ). This is a one-sided alternative hypothesis.

Example 10.5: Hypotheses with Two Samples of One Measurement Variable Section

Obesity is a major health problem today. Research is starting to show that people may be able to lose more weight on a low carbohydrate diet than on a low fat diet.

Research Question : Does the data suggest that, on the average, people are able to lose more weight on a low carbohydrate diet than on a low fat diet?
Response Variable : Weight loss (pounds)
Explanatory (Grouping) Variable : Type of diet
Null Hypothesis : There is no difference in the mean amount of weight loss when comparing a low carbohydrate diet with a low fat diet (population mean weight loss on a low carbohydrate diet = population mean weight loss on a low fat diet).
Alternative Hypothesis : The mean weight loss should be greater for those on a low carbohydrate diet when compared with those on a low fat diet (population mean weight loss on a low carbohydrate diet > population mean weight loss on a low fat diet). This is a one-sided alternative hypothesis.

Example 10.6: Hypotheses about the relationship between Two Categorical Variables Section

Research Question : Do the odds of having a stroke increase if you inhale second hand smoke ? A case-control study of non-smoking stroke patients and controls of the same age and occupation are asked if someone in their household smokes.
Variables : There are two different categorical variables (Stroke patient vs control and whether the subject lives in the same household as a smoker). Living with a smoker (or not) is the natural explanatory variable and having a stroke (or not) is the natural response variable in this situation.
Null Hypothesis : There is no relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is = 1).
Alternative Hypothesis : There is a relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is > 1). This is a one-tailed alternative.

This research question might also be addressed like example 11.4 by making the hypotheses about comparing the proportion of stroke patients that live with smokers to the proportion of controls that live with smokers.

Example 10.7: Hypotheses about the relationship between Two Measurement Variables Section

Research Question : A financial analyst believes there might be a positive association between the change in a stock's price and the amount of the stock purchased by non-management employees the previous day (stock trading by management being under "insider-trading" regulatory restrictions).
Variables : Daily price change information (the response variable) and previous day stock purchases by non-management employees (explanatory variable). These are two different measurement variables.
Null Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) = 0.
Alternative Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) > 0. This is a one-sided alternative hypothesis.

Example 10.8: Hypotheses about comparing the relationship between Two Measurement Variables in Two Samples Section

Calculation of a person's approximate tip for their meal

Research Question : Is there a linear relationship between the amount of the bill (\$) at a restaurant and the tip (\$) that was left. Is the strength of this association different for family restaurants than for fine dining restaurants?
Variables : There are two different measurement variables. The size of the tip would depend on the size of the bill so the amount of the bill would be the explanatory variable and the size of the tip would be the response variable.
Null Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the same at family restaurants as it is at fine dining restaurants.
Alternative Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the difference at family restaurants then it is at fine dining restaurants. This is a two-sided alternative hypothesis.

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7 Addressing the Null & Alternate Hypotheses

Forming Hypotheses

After coming up with an experimental question, scientists develop hypotheses and predictions.

The null hypothesis H 0 states that there will be no effect of the treatment on the dependent variable, while the alternate hypothesis H A states the opposite, that there will be an effect.

Every hypothesis should include the following information:

Name of organism (common and Latin name)
Name of variable being manipulated (independent variable) with units
Which response will be measured (dependent variable) with units

Example of Null and Alternate Hypotheses

Null hypothesis (H 0 ) : Temperature ( o C) will have no effect on the pulse rate, measured in beats per minute, of mice ( Mus musculus ).

Alternate hypothesis (H A ) : Temperature ( o C) will have an effect on the pulse rate, measured in beats per minute, of mice ( Mus musculus ).

Reject or Fail to Reject the Null Hypothesis

To determine if two groups are different from one another, we look to see whether or not their respective 95% confidence intervals overlap and then relate this conclusion back to our two hypotheses.

If the 95% confidence intervals of two sample means do overlap (e.g., a treatment and the control), we are less than 95% sure (i.e. not sure enough) that these two groups reflect a true difference in the populations. This results in a failure to reject the null hypothesis , as there is insufficient evidence to support our alternative hypothesis that there was an effect.

If the 95% confidence intervals do not overlap, we are 95% sure that these two groups reflect a true difference in the populations. This result allows us to reject our null hypothesis and provide support for our alternative hypothesis. It should be noted that calculating confidence intervals only allows us to compare two groups at one time.

Interpreting Confidence Intervals

For example, the 95% confidence intervals of the 30 o C and 35 o C degrees treatment groups do not overlap with the confidence intervals of the 25 o C (control) (Figure 1). In this case, we reject the null hypothesis and provide support for the alternate hypothesis. We conclude that temperature ( o C) will have an effect on the pulse rate, measured in beats per minute, of mice ( Mus musculus ).

How to Address the Null and Alternate Hypotheses in the Discussion

In the Discussion section of your report you will need to discuss whether or not the 95% confidence intervals of the treatment groups overlap with the control.

When addressing the null and alternate hypothesis in the Discussion:

State whether the confidence intervals overlap with the control (be specific about which treatment(s) overlap).
If you reject or fail to reject the null hypothesis (use this language).
A full restatement of the supported hypothesis.

Click on the hotspots below to learn about how to address the null and alternate hypotheses in the Discussion.

How to Address the Null & Alternate Hypotheses in the Discussion

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Null Hypothesis Examples

The null hypothesis (H 0 ) is the hypothesis that states there is no statistical difference between two sample sets. In other words, it assumes the independent variable does not have an effect on the dependent variable in a scientific experiment .

The null hypothesis is the most powerful type of hypothesis in the scientific method because it’s the easiest one to test with a high confidence level using statistics. If the null hypothesis is accepted, then it’s evidence any observed differences between two experiment groups are due to random chance. If the null hypothesis is rejected, then it’s strong evidence there is a true difference between test sets or that the independent variable affects the dependent variable.

The null hypothesis is a nullifiable hypothesis. A researcher seeks to reject it because this result strongly indicates observed differences are real and not just due to chance.
The null hypothesis may be accepted or rejected, but not proven. There is always a level of confidence in the outcome.

What Is the Null Hypothesis?

The null hypothesis is written as H 0 , which is read as H-zero, H-nought, or H-null. It is associated with another hypothesis, called the alternate or alternative hypothesis H A or H 1 . When the null hypothesis and alternate hypothesis are written mathematically, they cover all possible outcomes of an experiment.

An experimenter tests the null hypothesis with a statistical analysis called a significance test. The significance test determines the likelihood that the results of the test are not due to chance. Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01). But, even if the confidence in the test is high, there is always a small chance the outcome is incorrect. This means you can’t prove a null hypothesis. It’s also a good reason why it’s important to repeat experiments.

Exact and Inexact Null Hypothesis

The most common type of null hypothesis assumes no difference between two samples or groups or no measurable effect of a treatment. This is the exact hypothesis . If you’re asked to state a null hypothesis for a science class, this is the one to write. It is the easiest type of hypothesis to test and is the only one accepted for certain types of analysis. Examples include:

There is no difference between two groups H 0 : μ 1 = μ 2 (where H 0 = the null hypothesis, μ 1 = the mean of population 1, and μ 2 = the mean of population 2)

Both groups have value of 100 (or any number or quality) H 0 : μ = 100

However, sometimes a researcher may test an inexact hypothesis . This type of hypothesis specifies ranges or intervals. Examples include:

Recovery time from a treatment is the same or worse than a placebo: H 0 : μ ≥ placebo time

There is a 5% or less difference between two groups: H 0 : 95 ≤ μ ≤ 105

An inexact hypothesis offers “directionality” about a phenomenon. For example, an exact hypothesis can indicate whether or not a treatment has an effect, while an inexact hypothesis can tell whether an effect is positive of negative. However, an inexact hypothesis may be harder to test and some scientists and statisticians disagree about whether it’s a true null hypothesis .

How to State the Null Hypothesis

To state the null hypothesis, first state what you expect the experiment to show. Then, rephrase the statement in a form that assumes there is no relationship between the variables or that a treatment has no effect.

Example: A researcher tests whether a new drug speeds recovery time from a certain disease. The average recovery time without treatment is 3 weeks.

State the goal of the experiment: “I hope the average recovery time with the new drug will be less than 3 weeks.”
Rephrase the hypothesis to assume the treatment has no effect: “If the drug doesn’t shorten recovery time, then the average time will be 3 weeks or longer.” Mathematically: H 0 : μ ≥ 3

This null hypothesis (inexact hypothesis) covers both the scenario in which the drug has no effect and the one in which the drugs makes the recovery time longer. The alternate hypothesis is that average recovery time will be less than three weeks:

H A : μ < 3

Of course, the researcher could test the no-effect hypothesis (exact null hypothesis): H 0 : μ = 3

The danger of testing this hypothesis is that rejecting it only implies the drug affected recovery time (not whether it made it better or worse). This is because the alternate hypothesis is:

H A : μ ≠ 3 (which includes μ <3 and μ >3)

Even though the no-effect null hypothesis yields less information, it’s used because it’s easier to test using statistics. Basically, testing whether something is unchanged/changed is easier than trying to quantify the nature of the change.

Remember, a researcher hopes to reject the null hypothesis because this supports the alternate hypothesis. Also, be sure the null and alternate hypothesis cover all outcomes. Finally, remember a simple true/false, equal/unequal, yes/no exact hypothesis is easier to test than a more complex inexact hypothesis.

Adèr, H. J.; Mellenbergh, G. J. & Hand, D. J. (2007). Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing. ISBN 978-90-79418-01-5 .
Cox, D. R. (2006). Principles of Statistical Inference . Cambridge University Press. ISBN 978-0-521-68567-2 .
Everitt, Brian (1998). The Cambridge Dictionary of Statistics . Cambridge, UK New York: Cambridge University Press. ISBN 978-0521593465.
Weiss, Neil A. (1999). Introductory Statistics (5th ed.). ISBN 9780201598773.

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Indian J Crit Care Med
v.23(Suppl 3); 2019 Sep

An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors

Priya ranganathan.

1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India

The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.

How to cite this article

Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.

Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).

SAMPLE VERSUS POPULATION

A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).

The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.

HYPOTHESIS TESTING

A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.

STATISTICAL ERRORS

There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.

The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.

The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.

Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).

Table 1 gives a summary of the two types of statistical errors with an example

Statistical errors

In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.

Source of support: Nil

Conflict of interest: None

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

Collecting Data

Frequently Asked Questions

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.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "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."

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. It is only at this point that researchers 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 a number of 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 wisdom 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.

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.

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.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

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 that there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
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 sample of the population 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."
Complex hypothesis: "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."

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:

"Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
"There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

"Children who receive a new reading intervention will perform better than students who did not receive the intervention."
"Adults will perform better on a memory task than children."

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 it would be impossible or difficult to conduct an experiment . 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 then be used to look at how the variables are related. This type of 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.

A Word From Verywell

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.

Some examples of how to write a hypothesis include:

"Staying up late will lead to worse test performance the next day."
"People who consume one apple each day will visit the doctor fewer times each year."
"Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

The research question
The independent variable (IV)
The dependent variable (DV)
The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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 is Null Hypothesis? What Is Its Importance in Research?

Scientists begin their research with a hypothesis that a relationship of some kind exists between variables. The null hypothesis is the opposite stating that no such relationship exists. Null hypothesis may seem unexciting, but it is a very important aspect of research. In this article, we discuss what null hypothesis is, how to make use of it, and why you should use it to improve your statistical analyses.

What is the Null Hypothesis?

The null hypothesis can be tested using statistical analysis and is often written as H 0 (read as “H-naught”). Once you determine how likely the sample relationship would be if the H 0 were true, you can run your analysis. Researchers use a significance test to determine the likelihood that the results supporting the H 0 are not due to chance.

The null hypothesis is not the same as an alternative hypothesis. An alternative hypothesis states, that there is a relationship between two variables, while H 0 posits the opposite. Let us consider the following example.

A researcher wants to discover the relationship between exercise frequency and appetite. She asks:

Q: Does increased exercise frequency lead to increased appetite? Alternative hypothesis: Increased exercise frequency leads to increased appetite. H 0 assumes that there is no relationship between the two variables: Increased exercise frequency does not lead to increased appetite.

Let us look at another example of how to state the null hypothesis:

Q: Does insufficient sleep lead to an increased risk of heart attack among men over age 50? H 0 : The amount of sleep men over age 50 get does not increase their risk of heart attack.

Why is Null Hypothesis Important?

Many scientists often neglect null hypothesis in their testing. As shown in the above examples, H 0 is often assumed to be the opposite of the hypothesis being tested. However, it is good practice to include H 0 and ensure it is carefully worded. To understand why, let us return to our previous example. In this case,

Alternative hypothesis: Getting too little sleep leads to an increased risk of heart attack among men over age 50.

H 0 : The amount of sleep men over age 50 get has no effect on their risk of heart attack.

Note that this H 0 is different than the one in our first example. What if we were to conduct this experiment and find that neither H 0 nor the alternative hypothesis was supported? The experiment would be considered invalid . Take our original H 0 in this case, “the amount of sleep men over age 50 get, does not increase their risk of heart attack”. If this H 0 is found to be untrue, and so is the alternative, we can still consider a third hypothesis. Perhaps getting insufficient sleep actually decreases the risk of a heart attack among men over age 50. Because we have tested H 0 , we have more information that we would not have if we had neglected it.

Do I Really Need to Test It?

The biggest problem with the null hypothesis is that many scientists see accepting it as a failure of the experiment. They consider that they have not proven anything of value. However, as we have learned from the replication crisis , negative results are just as important as positive ones. While they may seem less appealing to publishers, they can tell the scientific community important information about correlations that do or do not exist. In this way, they can drive science forward and prevent the wastage of resources.

Do you test for the null hypothesis? Why or why not? Let us know your thoughts in the comments below.

The following null hypotheses were formulated for this study: Ho1. There are no significant differences in the factors that influence urban gardening when respondents are grouped according to age, sex, household size, social status and average combined monthly income.

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Research Paper Hypothesis

Last updated on: Mar 27, 2024

How To Write A Hypothesis In A Research Paper - A Simple Guide

By: Barbara P.

Reviewed By:

Published on: Mar 6, 2024

how to write a hypothesis for a research paper

Writing a good hypothesis can be tricky, especially for new researchers. If your hypothesis isn't clear, your research paper might confuse readers about what you're studying and what are the anticipated outcomes of your study.

This confusion not only makes your research less trustworthy but also makes it harder for others to repeat or build on your work.

This blog post is here to help you understand how to state a hypothesis in a research paper. We'll go through it step by step, so you can learn to craft the important parts, like how you identify variables and formulate a clear hypothesis.

With these skills, you can make sure your hypothesis is clear and can be tested. So, get ready to craft a strong hypothesis!

On this Page

What is a Hypothesis in Research?

A hypothesis in research paper is a clear and testable statement or prediction that proposes a relationship between two or more variables.

It serves as a foundation for scientific investigations, guiding researchers in designing experiments and collecting data to either support or refute the hypothesis.

Research Question vs. Hypothesis vs. Thesis Statement

Research Question, Hypothesis, and Thesis Statement are three distinct elements in the research process, each serving a specific purpose.

Here's a breakdown of their differences:

Components of a Hypothesis

If you are wondering what to write in a research hypothesis, here is the breakdown:

Different Types of Hypothesis

Hypotheses come in various forms, each tailored to address different aspects of research.

Simple Hypothesis

This hypothesis proposes a straightforward relationship between two variables. For example, "Increasing sunlight will lead to increased plant growth."

Complex Hypothesis

In contrast, a complex hypothesis involves multiple variables and intricate relationships. An example could be "The interaction of sunlight, soil quality, and water availability collectively influences plant growth."

Directional Hypothesis

A directional hypothesis predicts a specific outcome. For instance, "Higher levels of education will result in increased job satisfaction."

Non-directional Hypothesis

Conversely, a non-directional hypothesis suggests a relationship without specifying the expected direction. An example is "There is a correlation between exercise and weight loss."

Associative Hypothesis

This type suggests a relationship between variables without implying causation. For example, "There is an association between ice cream sales and drowning incidents."

Causal Hypothesis

Unlike associative hypotheses, causal hypotheses propose a cause-and-effect relationship. For instance, "Increasing water intake causes improvements in skin hydration."

Null Hypothesis (H0)

The null hypothesis assumes no effect or relationship between variables. An example is, "There is no significant difference in test scores between students who receive extra tutoring and those who do not."

Alternative Hypothesis

The alternative hypothesis suggests a specific effect or relationship. It contrasts with the null hypothesis. For instance, "There is a significant difference in test scores between students who receive extra tutoring and those who do not."

5 Steps of Writing a Strong Hypothesis

A strong hypothesis gives the reader a clear view of your research. In this section, we will explore the steps of writing a strong hypothesis in detail:

Step 1: Understand the Research Question

Before diving into hypothesis crafting, take time to comprehend your research problem . Break it down into its core components.

For instance, if your research question is,

"How does caffeine consumption affect students' test performance?":

Identify the Main Focus: Clearly pinpoint the main aspect of the research question. In this case, it's the impact of caffeine consumption.
Define Variables : Recognize the key variables involved. In our example, the independent variable is "caffeine consumption," and the dependent variable is "students' test performance."
Refine the Question: Ask yourself what specific information you want to uncover. Is it the overall effect, a comparison between different levels of caffeine intake, or perhaps the timing of consumption? This refinement sets the stage for a more focused hypothesis.

Step 2: Identify the Variables

Understanding the variables of your research is crucial for defining the key roles and what changes you're anticipating.

They are the backbone of your hypothesis and create a focused and meaningful research approach.

Independent Variable (The What You Tweak): Pinpoint the factor you're going to manipulate. For instance, if you're exploring the impact of fertilizer on plant growth, fertilizer becomes your independent variable.
Dependent Variable (The What You Measure): Identify the factor you're measuring, the one expected to change due to the manipulation. In the plant growth example, it could be the height of the plants after a specific period—this is your dependent variable.

Step 3: Formulate a Clear Statement

Precision is the key to shaping a concise and strong hypothesis. To create a well-structured hypothesis, condense your thoughts into a single, easy-to-follow sentence.

Also, do not forget to clearly express the expected connection between your independent and dependent variables.

Step 4: Consider the Type of Hypothesis

In this step, you decide on the type of your hypothesis—whether it's giving a specific prediction or leaving room for surprises.

Example of Directional Hypothesis: "Increasing product advertising will result in higher sales."
Example of Non-Directional Hypothesis: "There is a significant correlation between stress levels and job performance."

Step 5: Predict the Outcome

Predicting the outcome is like offering a sneak peek into the conclusion of your research narrative.

By following these five steps, you'll be well-equipped to create a strong and effective hypothesis, providing a solid foundation for your research.

Check out this example of hypothesis for a research paper for better understanding:

Example Of Hypothesis In Research Proposal PDF

How to Write a Null Hypothesis In A Research Paper

Writing a null hypothesis in a research paper involves stating a proposition that there is no significant difference or effect.

Here are some tips for writing a null hypothesis:

Reverse the Statement: Formulate the null hypothesis by reversing the statement of the research hypothesis to suggest no significant difference or effect.
Use Equality Sign: Express the null hypothesis using an equality sign, such as "equals" or "is not significantly different from."
Be Specific and Testable: Make the null hypothesis specific and testable, ensuring it can be evaluated through data analysis.
Consider the Context: Ensure that the null hypothesis is appropriate for the context of your research.

Here is an example of a null hypothesis:

How to Write an Alternative Hypothesis?

Writing an alternative hypothesis, also known as the research hypothesis, involves stating a proposition that suggests a significant difference or effect between variables.

Here are some tips for writing an alternative hypothesis:

Formulate a Prediction: Formulate a clear prediction or expectation regarding the relationship or effect between the variables.
Express the Relationship: Clearly express the anticipated relationship or effect using specific terms, such as "greater than," "less than," or "different from."
Use Inequality Sign: Utilize an inequality sign (>, <, ?) to represent the direction of the expected difference or effect.

Here's a PDF example for an alternative hypothesis:

How to Write an Alternative Hypothesis

In a nutshell, hypotheses aren't just words; they guide us in discovering new things. So, as you dive into your own research, use clear hypotheses to represent yours to illuminate your research question.

But if you face any problem in creating a meaningful hypothesis or any section of your research paper, get help from the top paper writing service online .

Our expert writers will help you in creating an outstanding research paper that will show your command over the topic.

So, don’t waste time! Get your research papers from experts today!

Barbara has a Ph.D. in public health from an Ivy League university and extensive experience working in the medical field. With her practical experience conducting research on various health issues, she is skilled in writing innovative papers on healthcare. Her many works have been published in multiple publications.

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Best Practices in Science

The Null Hypothesis

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Publications

Journals and Blogs

The null hypothesis, as described by Anthony Greenwald in ‘Consequences of Prejudice Against the Null Hypothesis,’ is the hypothesis of no difference between treatment effects or of no association between variables. Unfortunately in academia, the ‘null’ is often associated with ‘insignificant,’ ‘no value,’ or ‘invalid.’ This association is due to the bias against papers that accept the null hypothesis by journals. This prejudice by journals to only accept papers that show ‘significant’ results (also known as rejecting this ‘null hypothesis’) puts added pressure on those working in academia, especially with their relevance and salaries often depend on publications. This pressure may also be correlated with increased scientific misconduct, which you can also read more about on this website by clicking here . If you would like to read publication, journal articles, and blogs about the null hypothesis, views on rejecting and accepting the null, and journal bias against the null hypothesis, please see the resources we have linked below.

Most scientific journals are prejudiced against papers that demonstrate support for null hypotheses and are unlikely to publish such papers and articles. This phenomenon leads to selective publishing of papers and ensures that the portion of articles that do get published is unrepresentative of the total research in the field.

Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence, and an alternative. The journal of wildlife management , 912-923.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society . Series B (Methodological), 289-300.

Berger, J. O., & Sellke, T. (1987). Testing a point null hypothesis: The irreconcilability of p values and evidence. Journal of the American statistical Association , 82 (397), 112-122.

Blackwelder, W. C. (1982). “Proving the null hypothesis” in clinical trials. Controlled clinical trials , 3 (4), 345-353.

Dirnagl, U. (2010). Fighting publication bias: introducing the Negative Results section. Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism , 30 (7), 1263.

Dickersin, K., Chan, S. S., Chalmersx, T. C., Sacks, H. S., & Smith, H. (1987). Publication bias and clinical trials. Controlled clinical trials , 8 (4), 343-353.

Efron, B. (2004). Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. Journal of the American Statistical Association , 99 (465), 96-104.

Fanelli, D. (2010). Do pressures to publish increase scientists’ bias? An empirical support from US States Data. PloS one , 5 (4), e10271.

Fanelli, D. (2011). Negative results are disappearing from most disciplines and countries. Scientometrics , 90 (3), 891-904.

Greenwald, A. G. (1975). Consequences of Prejudice Against the Null Hypothesis. Psychological Bulletin , 82 (1).

Hubbard, R., & Armstrong, J. S. (1997). Publication bias against null results. Psychological Reports , 80 (1), 337-338.

I’ve Got Your Impact Factor Right Here (Science, February 24, 2012)

Johnson, R. T., & Dickersin, K. (2007). Publication bias against negative results from clinical trials: three of the seven deadly sins. Nature Clinical Practice Neurology , 3 (11), 590-591.

Keep negativity out of politics. We need more of it in journals (STAT, October 14, 2016)

Knight, J. (2003). Negative results: Null and void. Nature , 422 (6932), 554-555.

Koren, G., & Klein, N. (1991). Bias against negative studies in newspaper reports of medical research. Jama , 266 (13), 1824-1826.

Koren, G., Shear, H., Graham, K., & Einarson, T. (1989). Bias against the null hypothesis: the reproductive hazards of cocaine. The Lancet , 334 (8677), 1440-1442.

Krantz, D. (2012). The Null Hypothesis Testing Controversy in Psychology. Journal of American Statistical Association .

Lash, T. (2017). The Harm Done to Reproducibility by the Culture of Null Hypothesis Significance Testing. American Journal of Epidemiology .

Mahoney, M. J. (1977). Publication prejudices: An experimental study of confirmatory bias in the peer review system. Cognitive therapy and research , 1 (2), 161-175.

Matosin, N., Frank, E., Engel, M., Lum, J. S., & Newell, K. A. (2014). Negativity towards negative results: a discussion of the disconnect between scientific worth and scientific culture.

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

No result is worthless: the value of negative results in science (BioMed Central, October 10, 2012)

Negative Results: The Dark Matter of Research (American Journal Experts)

Neil Malhotra: Why No News Is Still Important News in Research (Stanford Graduate School of Business, October 27, 2014)

Null Hypothesis Definition and Example (Statistics How To, November 5, 2012)

Null Hypothesis Glossary Definition (Statlect Digital Textbook)

Opinion: Publish Negative Results (The Scientist, January 15, 2013)

Positives in negative results: when finding ‘nothing’ means something (The Conversation, September 24, 2014)

Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic bulletin & review , 16 (2), 225-237.

Unknown Unknowns: The War on Null and Negative Results (social science space, September 19, 2014)

Valuing Null and Negative Results in Scientific Publishing (Scholastica, November 4, 2015)

Vasilev, M. R. (2013). Negative results in European psychology journals. Europe’s Journal of Psychology , 9 (4), 717-730

Where have all the negative results gone? (bioethics.net, December 4, 2013)

Where to publish negative results (BitesizeBio, November 27, 2013)

Why it’s time to publish research “failures” (Elsevier, May 5, 2015)

Woolson, R. F., & Kleinman, J. C. (1989). Perspectives on statistical significance testing. Annual review of public health , 10 (1), 423-440.

Would you publish your negative results? If no, why? (ResearchGate, October 26, 2012)

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Open access
Published: 09 April 2024

Anger is eliminated with the disposal of a paper written because of provocation

Yuta Kanaya 1 &
Nobuyuki Kawai ORCID: orcid.org/0000-0003-0372-1703 2 nAff1

Scientific Reports volume 14 , Article number: 7490 ( 2024 ) Cite this article

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Human behaviour

Anger suppression is important in our daily life, as its failure can sometimes lead to the breaking down of relationships in families. Thus, effective strategies to suppress or neutralise anger have been examined. This study shows that physical disposal of a piece of paper containing one’s written thoughts on the cause of a provocative event neutralises anger, while holding the paper did not. In this study, participants wrote brief opinions about social problems and received a handwritten, insulting comment consisting of low evaluations about their composition from a confederate. Then, the participants wrote the cause and their thoughts about the provocative event. Half of the participants (disposal group) disposed of the paper in the trash can (Experiment 1) or in the shredder (Experiment 2), while the other half (retention group) kept it in a file on the desk. All the participants showed an increased subjective rating of anger after receiving the insulting feedback. However, the subjective anger for the disposal group decreased as low as the baseline period, while that of the retention group was still higher than that in the baseline period in both experiments. We propose this method as a powerful and simple way to eliminate anger.

Microdosing with psilocybin mushrooms: a double-blind placebo-controlled study

Federico Cavanna, Stephanie Muller, … Enzo Tagliazucchi

Adults who microdose psychedelics report health related motivations and lower levels of anxiety and depression compared to non-microdosers

Joseph M. Rootman, Pamela Kryskow, … Zach Walsh

Emotions and brain function are altered up to one month after a single high dose of psilocybin

Frederick S. Barrett, Manoj K. Doss, … Roland R. Griffiths

Experiment 1

Introduction.

The need to control anger has been of importance for a long time in human societies, as inferred by a philosopher in Imperium Romanum who had already explored how to cease being angry 1 . However, it can still be challenging to suppress anger effectively. Frequent, unregulated anger often leads to violence towards children 2 , which has become an increasingly prevalent issue. One study found that the global estimate for children experiencing any form of violence (physical, sexual, emotional, or a combination) in the past year is one billion children aged 2–17 years 3 . The number of child abuse cases in Japan has reportedly doubled in the past decade 4 . Children learn about appropriate emotional expression and behaviour from their parents 5 , and children who have been maltreated may lack the opportunity to learn how to regulate anger. Consequently, these maltreated children may have difficulty controlling their own anger 6 , recognising anger in others 7 , and tend to exhibit externalizing behaviour problems 8 . These studies suggest that parental anger regulation issues negatively affect children’s emotional competence. Therefore, an effective way of reducing anger has been examined throughout the years 9 .

However, simply attempting to suppress anger is usually not effective 10 . Both cognitive reappraisal and distraction (i.e., thinking about something other than provocative comments) could reduce anger; however, distraction could suppress anger only for a transient period of time 11 . Cognitive reappraisal refers to the reinterpretation or modification of the meaning of an unpleasant situation. Although reappraisal is considered as an effective way to reduce anger 12 , it requires greater cognitive effort 13 , 14 . Therefore, reappraisal under stressful situations which require cognitive load was not found to be effective in reducing anger as compared to non-stressful situations 15 . Self-distancing, which may be responsible for the anger-reducing effect of reappraisal 12 is also considered as an effective way to reduce anger. Nevertheless, self-distancing or reflection on one’s provocation from a distance is often not feasible, especially in the heat of the moment 13 .

Failure to reduce anger can lead an individual to think about a provocative event repeatedly. Such ruminations are often produced in a self-immersed, experiential manner 16 . Self-immersed experiential rumination can lead to reliving past provocative events 17 , thus maintaining or even increasing subjective anger and vascular responses 18 .

However, among the types of ruminations, writing down a provocation event does not always maintain or increase anger; instead, anger is suppressed depending on the way of writing. For instance, anger was suppressed when participants wrote down the anger-inducing event in a detached, informational, ‘cool’ manner. However, their anger was not suppressed (and was maintained or even increased) when they failed to write down the event in an analytical manner, and wrote it down in a ‘hot’ (emotional) manner 12 . Somewhat relevant here is the expressive writing technique 19 , which is frequently used in emotion-focused psychotherapy treatment 20 . It is believed to be effective in suppressing anger in clinical settings. However, only one experimental study using this technique has been conducted, wherein it was found that there was a significant likelihood of reduced anger when sentences about the emotion were written in the past tense 21 . These studies suggest that anger may be successfully suppressed if individuals are able to separate their internal experience of provocative events from their sense of self 22 . Healy et al. 23 reported that negative self-referential statements (‘my life is pointless’), when presented in a defused format (‘I am having a thought that my life is pointless’), could decrease the emotional discomfort related to that statement.

These previous studies emphasised the cognitive processes (such as goals or valuations) that occur almost entirely inside individuals’ heads 24 . However, if we look at the literature more broadly, studies on emotion regulation (a situated cognitive approach) have demonstrated successful emotion control through dynamic interplay between the person and the situation 24 , 25 . From this situated cognition perspective, people perceive their environment in terms of the possibilities for the kinds of actions that they would pursue. These functional features of the environment (affordances) do not solely exist inside an individual’s mind but instead have a physical reality that exists in the individual’s relationship with the environment. For instance, people frequently use physical substances to modify their moods. People may take a hot shower when they feel lonely 26 , 27 or hold a teddy bear when they feel afraid 28 . Such access to physical objects can significantly modify individuals’ ability to manage their emotions.

In this study, we developed a new anger reduction strategy inspired by the situated cognition approach to emotion regulation 24 . Relevant to this approach, the notion of a grounded procedure of separation 29 also assumes that mental representations and functions are grounded in one’s own experiences and interactions with physical reality. For instance, if people want to take revenge through permanent removal (e.g. hatred for ex), they may destroy a related entity such that it is no longer recognisable (burn, melt, or tear related). In a related study, Briñol et al. 30 reported that writing down negative thoughts about a Mediterranean diet on a piece of paper and disposing of the paper in a trash can result in lower negative (more positive) evaluations of the diet, compared to a group that kept the paper in a booklet. These attitude changes may derive from the cognitive fusion that people often fuse with physical objects, such as jewellery, cars, and family heirlooms 31 . Such fused objects are valued more and are less likely to be abandoned because doing so means losing a part of themselves 32 , 33 . Specifically, throwing an object associated with negative emotions (anger) may result in losing the negative emotions (anger). However, to the best of our knowledge, no study has tested whether the disposal of anger-written paper can reduce or even eliminate anger.

Previous studies from a situated cognitive approach to anger management have changed the external environment of the individual in anger. Tool (object) use has received scant attention in these situated cognition approaches to anger management, except for a few studies, such as hitting a punching bag 34 and playing a video game 35 . This study examined a method in which the disposal of a paper (object) on which participants wrote down their descriptions or thoughts about a provocative event could neutralise anger. Participants threw the anger-written paper into a trash box in Experiment 1, and put the paper into a shredder in Experiment 2. If the action of disposal is crucial to modifying emotions, anger would be reduced only in participants in Experiment 1 but not in Experiment 2, as predicted by the grounded separation procedure 29 . Nevertheless, if anger was modified by the meaning of disposal, the subjective ratings of anger would be eliminated in both experiments. The disposal of the paper with the written descriptions would remove the psychological existence of anger for the provoked participants along with the disposal of paper by the dynamic interactions with the object 24 . This simple method of eliminating anger could potentially contribute to effective parental anger management toward their children.

Materials and method

Participants.

A total of 57 students (women = 21, mean age = 21.11, SD = 1.05) from a local university participated in this experiment. The data from seven participants were excluded from the final analysis because they correctly guessed the purpose of the experiment and they did not express induced anger by insult (subjective ratings of anger were lower or the same compared to those of the baseline), as was the case in a previous study 36 . Our final analysis included 50 participants (women = 16, mean age = 21.10, SD = 1.08). A sample size of 50 participants was determined by G*Power 3.1.9.4 37 using the a priori procedure for repeated measures ANOVA, within (periods)—between (disposal and retention) interaction with the parameters of 95% power, an expected effect size of 0.25 (defined as a medium effect by Cohen 38 ), alpha level of 0.05, a within-subjects measurement correlation of 0.5, and a nonsphericity correction ε of 1. The calculation suggested a sample size of 22 participants in each group. Based on these analyses, we concluded that the sample size was appropriate for this study.

Angry feelings were assessed with five adjective items: angry, bothered, annoyed, hostile, and irritated. These adjectives were previously used as measures of self-reported anger 39 . In this study, each response scale ranged from 1 (not at all) to 6 (extremely). As was the case in a previous study on anger 40 , scores on these five adjectives were averaged to form an anger experience composite, which was the score used in the analysis (Cronbach’s α = 0.90). We also used Positive and Negative Affect Schedule (PANAS) as a subjective scale to assess mainly negative feelings 38 . We used the Japanese version of the 6-point PANAS scale 41 .

In this experiment, participants' subjective emotional states were measured at three time points (baseline, post-provocation, and post-writing). The participants were told to write an essay on social problems (e.g., smoking in public) for which they would receive feedback from a doctoral student assessing the quality of the essay. They had seen the doctoral student before entering the experimental room. After the participants wrote the essay, they completed the PANAS and anger questionnaires for the baseline. The evaluation by the fictitious doctoral student was then provided to the participants. The evaluation included ratings of the essay on six characteristics using a 9-point scale (e.g. for intelligence, 1 = unintelligent, 9 = intelligent). All participants were given the following ratings: intelligence = 3, interest = 3, friendliness = 2, logic = 3, respectability = 4, and rationality = 3. Each essay was also provided with the following comment: ‘I cannot believe an educated person would think like this. I hope this person learns something while at the university’ 40 , 42 . All of these manipulations were successfully used in our previous study 40 . The participants were required to read the feedback ratings and comments silently for two minutes. Then, they filled out the subjective emotional questionnaires (PANAS and anger adjectives) for the post-provocation period.

Then, the participants were asked to write every thought of them on receiving the feedback and were given three minutes for this. The instruction was ‘Think about the event from your own perspective. Concentrate especially on the things that originally triggered the emotions and your reactions’. We added guide questions (‘Why were you feeling this way?’, ‘What made you feel this way?’) to induce analytical rumination. To allow the participants to write about their honest feelings, they were informed that the written paper would not be seen by anyone, including the experimenter. After writing, the participants were asked to review the sentences carefully for 30 s. For the retention group, the paper was turned over, put in a clear plastic folder, and placed on the right side of the desk. The participants in the disposal group rolled up the paper into a crumpled ball, stood up, threw the paper into the trash can held by the experimenter, and sat back in the chair. Finally, both groups of participants filled out the subjective emotional questionnaires (anger adjectives and PANAS) for the post-writing period. At the end of the experiment, all participants were debriefed and informed of the truth. They were also assured that the evaluations of their essays had been prepared in advance.

Data analyses

Angry feelings were analysed using a 2 (group: disposal or retention) × 3 (period: at baseline, post-provocation, and post-writing) ANOVA. All significance levels were set at p < 0.05. We used the Greenhouse–Geisser correction when Mauchly’s test of sphericity was violated. When the interaction was significant, multiple comparisons using the Bonferroni correction method were used to assess the differences.

We also report Bayes factors (BFs) from the Bayesian repeated measures ANOVA in JASP 43 . For BFs, BF 10 values reflect the probability of an alternative relative to the null hypothesis. BFs greater than 3 indicate support for the hypotheses. A BF favouring the alternative over the null hypothesis (BF 10 ) offers strong evidence for the alternative hypothesis when it is over 10. Values less than 0.33 indicate support for the null hypothesis, and values between 0.33 and 3 indicate data insensitivity. We also reported 95% confidence intervals.

We aimed to examine (1) whether angry feelings resumed in the disposal group, and (2) whether angry feelings were different between the groups after the disposal or retention treatments. Our main interest was angry feelings, while we also verified PANAS scores using a 2 (group: disposal or retention) × 3 (period: at baseline, post-provocation, and post-writing) ANOVA.

Ethics statement

All participants were paid for their participation and had provided written informed consent in accordance with the procedures before participation. The study was approved by the Ethics Committee of the Department of Cognitive and Psychological Sciences at Nagoya University (201104-C-02–02). All methods were carried out in accordance with the ethical guidelines of the Declaration of Helsinki. All participants provided their written and informed consent prior to starting the study.

Anger experience

The left panel of Fig. 1 shows mean subjective ratings of anger for disposal and retention groups at three time points (baseline, post-provocation, and post-writing). Subjective ratings of anger of both groups increased at the post-provocation ( M disposal = 3.34, SD = 1.20, 95% CI [2.86, 3.82]; M retention = 3.45, SD = 1.11, 95% CI [3.00, 3.89]) from the baseline ( M disposal = 1.59, SD = 0.50, 95% CI [1.39, 1.79]; M retention = 1.78, SD = 0.71, 95% CI [1.50, 2.07]). Subjective ratings at the post-writing decreased from the post-provocation, however those of retention group were still higher than the baseline ( M retention = 2.64, SD = 0.95, 95% CI [2.26, 3.02]), while those of disposal group eliminated at the same level of the baseline ( M disposal = 1.87, SD = 0.71, 95% CI [1.59, 2.16]). A 2 (group: disposal or retention) × 3 (period: at baseline, post-provocation, and post-writing) mixed model analysis of variance (ANOVA) revealed a significant main effect of period [ F (2, 96) = 73.36, p < 0.001, partial η 2 = 0.60, BF 10 > 100], while a main effect of group was not significant [ F (1, 48) = 3.21, p > 0.05, partial η 2 = 0.06, BF 10 = 0.66]. The interaction between group and period was significant [ F (2, 96) = 3.12, p < 0.05, partial η 2 = 0.06, BF 10 = 1.17]. Multiple comparisons with the Bonferroni method revealed that the subjective anger was significantly higher at the post-provocation than those at the baseline ( p < 0.05), indicating that a provocative manipulation was exerted. Subjective ratings of anger post-writing decreased significantly, compared to post-provocation ( p < 0.05). Importantly, however, subjective ratings of retention group at the post-writing period were still significantly higher than those of the baseline period ( p < 0.05), whereas those of disposal group at the post-writing period eliminated to levels of the baseline period ( p > 0.05). Subjective ratings of disposal group at the post-writing period were significantly lower than those of retention group ( p < 0.01).

Self-reported anger during Experiment 1 (left) and Experiment 2 (right). Significant differences emerged at the end of time due to experimental manipulations. Possible values for anger range from 1 to 6. Each vertical line illustrates the 95% confidence intervals for each group.

Negative and positive affect

The negative affect subscale of the PANAS at post-provocation ( M disposal = 3.10, SD = 1.00, 95% CI [2.70, 3.49]; M retention = 3.06, SD = 1.03, 95% CI [2.64, 3.47]) was higher than at baseline ( M disposal = 2.45, SD = 0.66, 95% CI [2.18, 2.71]; M retention = 2.50, SD = 0.84, 95% CI [2.16, 2.83]) and post-writing ( M disposal = 2.06, SD = 0.65, 95% CI [1.80, 2.32]; M retention = 2.39, SD = 0.88, 95% CI [2.04, 2.73]). The 95% CIs of the disposal group overlapped a little bit between post-provocation [2.70, 3.49] and baseline periods [2.18, 2.71], and those of the retention group overlapped between both the post-provocation [2.64, 3.47] and baseline [2.16, 2.83]. The 95% CIs for the post-writing means partially overlapped between the groups. A 2 (group) × 3 (period) mixed ANOVA revealed a significant main effect of period [ F (2, 96) = 28.64, p < 0.001, partial η 2 = 0.37, BF 10 > 100]. However, the main effect of group [ F (1, 48) = 0.29, p > 0.05, partial η 2 = 0.01, BF 10 = 0.32] and the interaction between group and period were not significant [ F (2, 96) = 1.35, p > 0.05, partial η 2 = 0.03, BF 10 = 0.31]. Multiple comparisons with the Bonferroni method revealed that the subjective negative affect post-provocation was significantly higher than at baseline and post-writing ( ps < 0.05).

The PANAS positive affect subscale showed little variation at three periods ( M disposal = 2.33, SD = 0.80, 95% CI [2.01, 2.65]; M retention = 2.32, SD = 0.75, 95% CI [2.01, 2.62]), post-provocation ( M disposal = 2.44, SD = 0.76, 95% CI [2.13, 2.75]; M retention = 2.42, SD = 0.89, 95% CI [2.06, 2.78]), and post-writing ( M disposal = 2.38, SD = 0.87, 95% CI [2.03, 2.73]; M retention = 2.27, SD = 0.83, 95% CI [1.93, 2.60]). A 2 × 3 mixed ANOVA revealed that neither main effects nor interaction was significant ( Fs < 0.90, ps > 0.41, BF 10 s < 0.14).

This study examined whether writing about the provocative event and disposing of the paper into a trash can would suppress anger. The provocation treatments evoked anger in both the groups similarly. Nevertheless, the retention group still showed significantly higher anger compared to levels at the baseline period, while the disposal group completely eliminated their anger after the disposal of the anger-written paper. These results suggest that the disposal of the paper containing ruminated anger into the trash can neutralise anger. Our interpretation is that the act of throwing the paper with ruminated anger into the trash can produces a feeling similar to the psychological existence (anger) being discarded, leading to anger elimination, since the psychological entity (anger) was disposed along with the physical object (anger-written paper).

One may argue that it was not the disposal itself but the physical distance played a critical role in reducing anger. Since the paper was distanced from participants in the disposal group, whereas the paper in the retention group was located by them. Nevertheless, Zhang et al. 44 showed that engaging in an avoidance action rather than creating physical distance was critical for reversing the perceived effect of negative thoughts. In their study (Experiment 5), participants in avoidance action conditions either threw the ball to the opposite corner of the room (creating physical distance between themselves and the ball), or pretended to throw the ball (creating no distance between themselves and the ball). Participants in the no-avoidance action condition either carried the ball to the opposite corner of the room and left it there (creating physical distance between the self and the ball without involving a throwing action) or held the ball in their non-dominant hand (creating no distance). Participants in both avoidance action conditions reversed the negative thoughts, while participants in both no-avoidance conditions did not. Avoidance actions were crucial in their study. Therefore, the physical distance would not contribute to reduce anger in this study. However, disposal action might be the key to neutralising anger in this study. Nevertheless, we assume that the meaning (i.e. interpretation) of disposal is more important than the action itself. Other studies have also suggested that the meaning of an action is critical for determining its impact, not the action itself 30 , 45 . This study could not exclude throwing action's potential contribution to neutralising anger. Thus, we conducted another experiment to exclude the potential contribution of the throwing action as much as possible, confirm the effectiveness of the disposal method, and explore the variation in this method.

Experiment 2

Experiment 1 indicated that the disposal of a piece of paper containing the description of an anger-inducing experience into the trash can neutralise anger. However, it was unclear what aspect of the paper’s disposal neutralised anger. Although we interpreted the meaning of the action as critical to neutralising anger, the physical distance between the participant and the paper or the action itself (i.e. embodied cognition) might have played a critical role. We set up the second experiment: (1) to replicate the results of Experiment 1; (2) to exclude the embodied explanation as much as possible; and (3) to explore another version of the disposal method using a shredder on the desk. In this experiment, we asked participants to put the paper containing anger into the shredder instead of throwing it into the trash can which was kept at some distance from the participants. We also made a small change to the retention group. Participants of retention group put the paper into a clear box on the desk, and the disposal group put the paper into the shredder. Thus, the distance between the participants and the paper and the type of action were matched between the two groups. If the sensorimotor experience of throwing the paper was critical to neutralise anger, we would not be able to replicate the results of Experiment 1. Nevertheless, if the meaning of the disposal of a physical entity plays a critical role in reducing anger, we anticipated obtaining similar results. In line with our prediction, the attitude changed when the paper was transferred to a box labelled ‘trash can’, which indicated mentally discarding it, compared to a box labelled ‘safety box’ 46 , suggesting that the perceived meaning of actions, and not the actions per se, influence attitude change. Hence, we designed a new study to confirm whether the perceived meaning of action eliminates anger. We predicted that putting the paper in a shredder would reduce negative emotions (anger), as compared to keeping the paper.

A total of 48 participants (women = 24, mean age = 26.81, SD = 9.42) were participated through worker dispatching company and a local university. There was no overlap between the participants of the two experiments. This sample size was determined using G*Power 3.1.9.4 37 using the a priori procedure for repeated measures ANOVA, within (periods)–between (disposal and retention) interaction with the parameters of 95% power, an expected effect size of 0.25 (defined as a medium effect by Cohen 38 ), alpha level of 0.05, a within-participants measurement correlation of 0.5, and a nonsphericity correction ε of 1. The calculation suggested a sample size of 22 participants in each group. Based on these analyses, we concluded that the sample size was appropriate for this study. As in Experiment 1, the data of two participants were excluded from the final analysis because they correctly guessed the purpose of the experiment and did not express anger by insult (subjective ratings of anger were lower or the same as those at the baseline). Our final analysis included 46 participants (women = 23, mean age = 26.39, SD = 9.14).

As in Experiment 1, angry feelings were assessed using five adjectives: angry, bothered, annoyed, hostile, and irritated. Responses ranged from 1 (not at all) to 6 (extremely). Scores on these five adjectives will be averaged to form an anger experience composite, which is the score used in the analyses. We also used the Japanese version of the 6-point PANAS scale as a subjective scale to assess mainly negative feelings 40 , 41 .

For the disposal group, a dustbin-type shredder (ACCO Brands Japan Corp, GSHA26MB) was used. This shredder (30 cm × 10 cm × 28 cm) cuts paper into pieces of 2 mm × 14 mm on putting the paper in from the top. The lower part of the shredder holds a transparent dustbin, so that the pieces of paper can be observed from the outside. For the retention group, a hand-made clear plastic box (23 cm × 5 cm × 30 cm) was used. Paper can be placed from the top, as with the shredder. Furthermore, as with the lower part of the shredder, the box is also transparent so that the paper in the box can be observed from the outside.

This experiment followed the same method used in Experiment 1 with slight changes. The words “while at university” were removed from the provocative comment (‘I cannot believe an educated person would think like this. I hope this person learns something while at university’ 40 , 42 , because non-students participated in this study. The second change was the method of disposing or retaining the paper containing a description of the anger-inducing experience. After participants wrote down provocative events in an analytical manner, a transparent box or a transparent shredder bin was placed on the desk in front of them (Fig. 2 ), before they were asked to review the sentences carefully for 30 s. Then, participants were required to put the paper into the box, with the frontside of the paper facing them. Participants in the disposal group watched as the paper was cut in the shredder for five seconds. Participants in the retention group were required to enclose the paper in a clear file folder and place it in a transparent box showing their written sentences. Then, they observed the paper carefully for five seconds. Subsequently, the box was turned back to show the blank side of the paper. All participants rated their anger and provided responses to the PANAS after these treatments.

Pictures of experimental manipulations in Experiment 2. The disposal group (left) put the paper into the shredder, while the retention group (right) put the paper into the transparent box.

The right panel of Fig. 1 shows the mean subjective anger ratings for the disposal and retention groups at the three time points (baseline, post-provocation, and post-writing). This pattern of results is similar to that of Experiment 1. Subjective ratings of anger in both groups increased after provocation ( M disposal = 3.14, SD = 1.38, 95% CI [2.56, 3.72]; M retention = 3.24, SD = 1.04, 95% CI [2.80, 3.67]) from baseline ( M disposal = 1.57, SD = 0.75, 95% CI [1.25, 1.88]; M retention = 1.64, SD = 0.59, 95% CI [1.40, 1.89]). Subjective ratings at post-writing decreased from post-provocation. However, those of the retention group were still higher than those of the baseline ( M retention = 2.75, SD = 1.05, 95% CI [2.31, 3.19]), while those of the disposal group were eliminated at the same level as the baseline ( M disposal = 1.98, SD = 0.87, 95% CI [1.62, 2.35]). Only a small overlap (0.04) was observed in the 95% CI for the mean post-writing scores between the groups. A 2 (group: disposal or retention) × 3 (period: at baseline, post-provocation, and post-writing) mixed model ANOVA revealed a significant main effect of period [ F (2, 88) = 56.93, p < 0.001, partial η 2 = 0.56, BF 10 > 100], while the main effect of group was not significant [ F (1, 44) = 1.68, p > 0.05, partial η 2 = 0.04, BF 10 = 0.46]. The interaction between group and period was significant [ F (2, 88) = 3.49, p < 0.05, partial η 2 = 0.07, BF 10 = 1.62]. Multiple comparisons with the Bonferroni method revealed that subjective anger was significantly higher at post-provocation than baseline ( p < 0.05), indicating that provocative manipulation was exerted. Subjective ratings of anger at post-writing decreased significantly compared to post-provocation ( p < 0.05). However, the subjective ratings of the retention group in the post-writing period were still maintained at the same level of anger as those of the post-provocation period ( p > 0.05). Contrastingly, those of the disposal group in the post-writing period were significantly lower than those of the post-provocation period ( p < 0.05).

Additionally, as was the result of Experiment1, the subjective ratings of the retention group in the post-writing period were significantly higher than those of the baseline period ( p < 0.05). Those of the disposal group in the post-writing period were eliminated to the baseline period ( p > 0.05). The subjective ratings of the disposal group in the post-writing period were significantly lower than those of the retention group ( p < 0.05).

The negative affect subscale of the PANAS at post-provocation ( M disposal = 3.34, SD = 1.09, 95% CI [2.88, 3.79]; M retention = 3.35, SD = 0.89, 95% CI [2.98, 3.73]) was higher than at baseline ( M disposal = 2.60, SD = 0.78, 95% CI [2.27, 2.93]; M retention = 2.73, SD = 0.92, 95% CI [2.34, 3.11]) and post-writing ( M disposal = 2.45, SD = 0.96, 95% CI [2.05, 2.85]; M retention = 2.57, SD = 0.87, 95% CI [2.20, 2.93]). The 95% CIs of the disposal group overlapped a little bit between post-provocation [2.88, 3.79] and baseline periods [2.27, 2.93], and those of the retention group overlapped between both the post-provocation [2.98, 3.73] and baseline [2.34, 3.11]. A 2 (group) × 3 (period) mixed ANOVA revealed a significant main effect of period [ F (2, 88) = 20.19, p < 0.01, partial η 2 = 0.68, BF 10 > 100]. However, the main effect of the group [ F (1, 44) = 0.15, p > 0.05, partial η 2 = 0.06, BF 10 = 0.33] and the interaction between group and period were not significant [ F (2, 88) = 1.35, p > 0.05, partial η 2 = 0.05, BF 10 = 0.13]. Multiple comparisons with the Bonferroni method revealed that the subjective negative affect post-provocation was significantly higher than at baseline and post-writing ( ps < 0.05).

The positive affect subscale of the PANAS showed little variation at the three-time points ( M disposal = 2.88, SD = 1.03, 95% CI [2.44, 3.31]; M retention = 2.57, SD = 0.89), 95% CI [2.19, 2.94], post-provocation ( M disposal = 2.49, SD = 0.86, 95% CI [2.13, 2.85]; M retention = 2.51, SD = 0.94, 95% CI [2.12, 2.90]), and post-writing ( M disposal = 2.49, SD = 0.97, 95% CI [2.08, 2.89]; M retention = 2.64, SD = 1.02, 95% CI [2.21, 3.06]). A 2 × 3 mixed ANOVA revealed that neither the main effects nor interaction were significant ( Fs < 2.28, ps > 0.11, BF 10 s < 0.70).

The results were essentially the same as those of Experiment 1. The disposal group significantly reduced their anger after disposing of the anger-written paper into the shredder. The retention group showed significantly higher anger than the baseline period and disposal group. These results suggest that the results in Experiment 1 could be attributed neither to the physical distance between the participant and the paper nor to the action itself (i.e. embodied cognition). Specifically, Experiment 2 replicated the results of Experiment 1 and excluded the embodied explanation (the sensorimotor experience of throwing the paper) because the action of the disposal group was quite similar to that of the retention group in Experiment 2. The distance between participant and paper was the same in both groups, as the transparent box and shredder were placed on the desk.

General discussion

This study aimed to determine whether the disposal of anger-written papers could eliminate or at least reduce subjective anger. Disposal manipulation eliminated anger, either by throwing the paper into a trash can or placing it into the shredder. We propose that this anger reduction method is quite effective, so the subjective ratings of anger resumed as much as the baseline levels. We believe that this method can be used in daily life and especially for populations characterised by extreme levels of anger and aggression in their home. The use of this method may potentially contribute to emotion socialization, as parents are the primary model for their children.

These results indicate that the sensorimotor experience of throwing paper plays a small role in reducing subjective anger 44 . Instead, the meaning (interpretation) of disposal plays a critical role. These results are consistent with other studies which showed that the meaning of disposal was critical for determining its impact, not the action itself 30 , 45 . However, these results are partially inconsistent with those reported by Zhang et al. 44 . Their experiment tested whether certain behaviors could lower the perceived likelihood of bad luck, as is often the case with jinxes. Participants who threw a ball believed that a jinxed-negative outcome was less likely than those who held the ball. They demonstrated that engaging in an avoidant action rather than creating physical distance was critical for reversing the perceived effect of the jinx. The results of Experiment 1 in this study are consistent with their results. However, we demonstrated that neither avoidance action nor physical distance was crucial in reducing subjective anger.

Our results may be related to the phenomenon of ‘backward magical contagion’ 47 , which is the belief that actions taken on an object (e.g. hair) associated with an individual can affect the individuals themselves. Rozin et al. 48 discovered that individuals experience strong negative emotions when their personal objects are possessed by negative others (such as rapists or enemies). However, these emotions are reduced when the objects are destroyed, such as throwing them in a septic tank or burning them. The phenomenon of ‘magical contagion’ or ‘celebrity contagion’ refers to the belief that the ‘essence’ of an individual can be transferred to their possessions. This backward magical contagion operates in a reversed process, where manipulating an object associated with a person is thought to impact the individuals themselves. The current study's findings may be explained by the concept of backward magical contagion, which posits that negative emotions can be transferred from others to an individual through their possessions. This study did not involve the direct mediation of other individuals. The neutralization of subjective anger through the disposal of an object may be achieved by recognizing that the physical entity, such as a piece of paper, has been diminished, thus causing the original emotion to also disappear.

At least, however, some limitations regarding this disposal method should be addressed in future studies. First, the findings of this study are based on the assumption that participants identified their subjective anger with the paper. Thus, subjective anger had gone with the anger-written paper after its disposal. The participants were asked to review the sentences carefully for 30 s to enhance this identification between thought and paper. It is not clear whether this review process is necessary for identification.

Another limitation is that we did not test a digital device, such as a word processor or smartphone, but used only papers. We believe the present disposal method can be generalised to a digital device, whereas empirical data are limited only by physical entities, papers, trash cans, or shredders. Suppose the disposal method is proven to be effective in digital devices. In that case, it will be adopted in various situations, such as business meetings or daily conversations in schools, by writing and disposing of with a smartphone.

Furthermore, although the disposal method had a more significant effect so that the subjective ratings of anger were eliminated as much as the baseline levels, the effectiveness of this method was not directly compared to other anger reduction methods, such as self-distancing. Other methods may be as effective or even more effective than the present disposal method. Personality traits may modulate the effects of anger suppression, although this has not been examined in the techniques used in this or in other studies. Individuals with high (versus low) levels of trait anger tended to experience lapses in effortful control when exposed to anger-relevant stimuli 49 , 50 . As mentioned above, although cognitive reappraisal (the reinterpretation of the meaning of an unpleasant event) is considered an effective way to reduce anger 12 , it requires more significant cognitive effort 13 , 14 . Self-distancing is not feasible, particularly during the heat of the moment 13 . Conversely, the disposal method with low cognitive effort used in this study may be more effective for individuals with lower levels of trait self-control than for those with high trait self-control. Future research should examine whether personality traits moderate the relationship between the disposal method and the expected outcomes.

Individuals with higher levels of trait anger tended to have prolonged experiences of induced state anger 51 . However, experimental research on anger regulation strategies has predominantly emphasized the effectiveness of immediate control 10 , 11 , 12 , neglecting to investigate whether these strategies are equally effective in managing anger that persists over time. However, in everyday life, it is not always feasible to implement anger regulation strategies immediately after anger arises. Therefore, to ascertain its practical utility in real-world settings, it is imperative to examine whether the effectiveness of the disposal method varies with the duration of anger.

Moreover, it should be tested whether the disposal method can suppress subjective anger even if participants write down a provocation event in an experiential manner rather than in the analytic rumination manner used in this study. Previous studies suggest that anger rumination can maintain 52 or even increase 53 the original level of anger when participants wrote down a provocation event in an experiential rumination manner. As it may not be easy to write down analytically, especially in the heat of the moment, the disposal method will gain further strength if it is valid by experiential rumination.

It should be mentioned that although provocation was effective in both the subjective anger score and the PANAS negative score, the revealed emotion regulation strategy in this study seemed specific to anger (as no significant interaction effect for the PANAS negative score was observed). Kubo et al. 40 reported that the increase in the state of anger relevant to approach motivation (aggression) by provocation (measured using the STAXI and asymmetry of prefrontal brain activity) was reduced by an apology comment. However, an increase in the subjective scores of negative emotion (assessed using the PANAS) remained unchanged, regardless of the presence or absence of an apology comment. They proposed anger as not a unitary process but one that comprises multiple independent components (subjective anger and negative feelings). If the anger scale used in this study reflects the approach motivation component of anger as well as the STAXI, the disposal method appears to specifically suppress the components of anger’s approach motivation (aggression) and can be used to reduce aggression as a clinical technique.

Despite these limitations, this is the first study to be designed and used to conveniently eliminate subjective anger by interacting with physical entities. It offers a cost-effective and easy-to-use method to reduce anger by rumination about the provocative event, which otherwise lasts longer. Anyone with a pen and piece of paper can use this method. Suppose one maintains a diary or a personal log. In that case, they can write down a provocative event on the day on the memo pad, and throwing it into the trash can eliminate the provocative event. This action may help neutralize the negative emotions associated with the event, potentially protecting the children’s emotional socialization.

This study presents a new and convenient method for eliminating subjective anger. This method offers a cost-effective way to eliminate anger in various situations, including business meetings, childcare, and clinical applications. The building blocks of this method (e.g. applying it to a digital device or creating a specific application) could be useful in various daily situations as well as behavioural therapies. In particular, for someone who has difficulty suppressing their anger in their homes.

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

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This study was supported by JSPS KAKENHI Grant Numbers 21K18552 and 21H04421, by Aoyama Gakuin University grant for ‘Projection Science,’ and by JST SPRING, Grant Number JPMJSP2125.

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Nobuyuki Kawai

Present address: Department of Cognitive and Psychological Sciences, Nagoya University, Nagoya, 464-8601, Japan

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Department of Cognitive and Psychological Sciences, Nagoya University, Nagoya, 464-8601, Japan

Yuta Kanaya

Academy of Emerging Science, Chubu University, Kasugai City, 487-8501, Japan

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N.K.: Conceptualization, Methodology, Writing-Original draft preparation, Writing-Reviewing and Editing, Supervision, Validation. Y.K.: Data collection and curation, Writing-Original draft preparation Visualization, Investigation.

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Kanaya, Y., Kawai, N. Anger is eliminated with the disposal of a paper written because of provocation. Sci Rep 14 , 7490 (2024). https://doi.org/10.1038/s41598-024-57916-z

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Published on 12.4.2024 in Vol 26 (2024)

The Effectiveness of a Digital App for Reduction of Clinical Symptoms in Individuals With Panic Disorder: Randomized Controlled Trial

Authors of this article:

Original Paper

KunJung Kim, MD ;
Hyunchan Hwang, MD, PhD ;
Sujin Bae, PhD ;
Sun Mi Kim, MD, PhD ;
Doug Hyun Han, MD, PhD

Chung Ang University Hospital, Seoul, Republic of Korea

Corresponding Author:

Doug Hyun Han, MD, PhD

Chung Ang University Hospital

102 Heucsock ro

Seoul, 06973

Republic of Korea

Phone: 82 2 6299 3132

Fax:82 2 6299 3100

Email: [email protected]

Background: Panic disorder is a common and important disease in clinical practice that decreases individual productivity and increases health care use. Treatments comprise medication and cognitive behavioral therapy. However, adverse medication effects and poor treatment compliance mean new therapeutic models are needed.

Objective: We hypothesized that digital therapy for panic disorder may improve panic disorder symptoms and that treatment response would be associated with brain activity changes assessed with functional near-infrared spectroscopy (fNIRS).

Methods: Individuals (n=50) with a history of panic attacks were recruited. Symptoms were assessed before and after the use of an app for panic disorder, which in this study was a smartphone-based app for treating the clinical symptoms of panic disorder, panic symptoms, depressive symptoms, and anxiety. The hemodynamics in the frontal cortex during the resting state were measured via fNIRS. The app had 4 parts: diary, education, quest, and serious games. The study trial was approved by the institutional review board of Chung-Ang University Hospital (1041078-202112-HR-349-01) and written informed consent was obtained from all participants.

Results: The number of participants with improved panic symptoms in the app use group (20/25, 80%) was greater than that in the control group (6/21, 29%; χ 2 1 =12.3; P =.005). During treatment, the improvement in the Panic Disorder Severity Scale (PDSS) score in the app use group was greater than that in the control group ( F 1,44 =7.03; P =.01). In the app use group, the total PDSS score declined by 42.5% (mean score 14.3, SD 6.5 at baseline and mean score 7.2, SD 3.6 after the intervention), whereas the PDSS score declined by 14.6% in the control group (mean score 12.4, SD 5.2 at baseline and mean score 9.8, SD 7.9 after the intervention). There were no significant differences in accumulated oxygenated hemoglobin (accHbO 2 ) at baseline between the app use and control groups. During treatment, the reduction in accHbO 2 in the right ventrolateral prefrontal cortex (VLPFC; F 1,44 =8.22; P =.006) and the right orbitofrontal cortex (OFC; F 1,44 =8.88; P =.005) was greater in the app use than the control group.

Conclusions: Apps for panic disorder should effectively reduce symptoms and VLPFC and OFC brain activity in patients with panic disorder. The improvement of panic disorder symptoms was positively correlated with decreased VLPFC and OFC brain activity in the resting state.

Trial Registration: Clinical Research Information Service KCT0007280; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=21448

Introduction

Panic disorder is a common and important disease in clinical practice that leads to a reduction of individual productivity and increased use of health care [ 1 ]. The lifetime prevalence of panic disorder in the general population is 4.8%, and 22.7% of people experience panic attacks [ 2 ]. The most common symptoms of panic disorder include palpitations, shortness of breath, chest pain, numbness of the hands and feet, and cardiorespiratory-type symptoms, in addition to fear of dying, sweating, tremors, dizziness, nausea, and chills [ 3 ]. The US Food and Drug Administration has currently only approved selective serotonin reuptake inhibitors (SSRIs) for the treatment of panic disorder [ 4 ]. However, it is clinically difficult to expect an improvement in symptoms using SSRIs alone in the acute phase; thus we treat patients with benzodiazepine, which can lead to dependence and withdrawal symptoms [ 5 , 6 ]. The most common side effects of SSRIs reported by patients are reduced sexual function, drowsiness, and weight gain [ 7 ], and clinicians may hesitate to use benzodiazepines due to dependence and withdrawal symptoms [ 8 ]. Cognitive behavioral therapy (CBT) is the most widely used nonpharmaceutical treatment for anxiety disorders [ 9 ]. Additional nonpharmaceutical treatments, such as group therapy and supportive psychotherapy, are also available for patients with panic disorder [ 10 , 11 ]. However, these treatments have the disadvantage of requiring face-to-face contact; therefore, other therapeutic alternatives should be offered to patients during pandemics such as COVID-19.

The definition of a digital therapeutic (DTx) is a therapeutic that delivers evidence-based interventions to prevent, manage, or treat a medical disorder or disease; DTxs are currently used in many areas [ 12 ]. This kind of medical and public health use of smartphones and digital technologies is also known as mobile health (mHealth). DTxs related to mental health medicine are actively used in various psychiatric disorders, such as insomnia, substance abuse, attention-deficit/hyperactivity disorder, and anxiety and depression, among others [ 13 ]. In particular, the use of Freespira, a panic disorder DTx, reduced panic symptoms, avoidance behaviors, and treatment costs in patients with panic disorder [ 14 ].

As brain imaging technology advances, a great deal of functional mapping information on the human brain has been accumulated from positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). Among these technologies, fNIRS can measure brain activity in a noninvasive and safe manner through measuring changes in the hemoglobin oxygenation state of the human brain [ 15 ]. Various studies have been conducted using fNIRS and fMRI to reveal correlations between panic disorder and brain regions. For example, patients with panic disorder show increased activity in the inferior frontal cortex, hippocampus, cingulate (both anterior and posterior), and orbitofrontal cortex (OFC) [ 16 ]. Previously, we confirmed that patients with panic disorder during rest periods showed increased activity in the OFC [ 17 ].

In this study, we determined whether an app for panic disorder would improve panic disorder symptoms. In addition, we used fNIRS to confirm the association between changes in panic disorder symptoms and changes in activity in specific brain regions.

Participants

Patients who had experiences of panic attacks were recruited between March 1 and July 30, 2022, through billboard advertisements at our hospital. The inclusion criteria for the study were as follows: (1) age between 20 and 65 years, (2) diagnosis of panic disorder based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and (3) ability to use apps without problems. The exclusion criteria were as follows: (1) a history of other psychiatric disorders, except for anxiety disorder, or substance dependence, except for habitual alcohol and tobacco use; and (2) a history of head trauma and chronic medical conditions. The research clinician assessed whether patients fulfilled the inclusion or exclusion criteria. Written informed consent was acquired from all participants at the first visit. This study has been registered with the Clinical Research Information Service (KCT0007280).

Assessment Scales for Anxiety Symptoms

The severity of panic symptoms was assessed using the Panic Disorder Severity Scale (PDSS). The PDSS was developed by Shear et al [ 18 ] in 1997. It is a 7-item instrument used to rate the overall severity of panic disorder and was validated in Korea by Lim et al [ 19 ] in 2001.

The anxiety symptoms of all participants were assessed using the clinician-based Hamilton Anxiety Scale (HAM-A) questionnaire and the participant-based Generalized Anxiety Disorder-7 (GAD-7) questionnaire. The HAM-A was developed by Hamilton in 1969 [ 20 ]. The 14-item version remains the most used outcome measure in clinical trials of treatments for anxiety disorders and was validated in Korea by Kim [ 21 ] in 2001.

The GAD-7 questionnaire, developed by Spitzer et al [ 22 ], is a 7-item self-report anxiety questionnaire designed to assess the patient’s health status during the previous 2 weeks. The GAD-7 was translated into the Korean language and is freely downloadable on the Patient Health Questionnaire website [ 23 ].

Hemodynamic Response of the Prefrontal Cortex

The hemodynamics in the frontal cortex during the resting state were measured using the fNIRS device (NIRSIT; OBELAB Inc). The NIRSIT has 24 laser diodes (sources) emitting light at 2 wavelengths (780 nm and 850 nm) and 32 photodetectors with a sampling rate of 8.138 Hz [ 24 ]. The distance between the source and photodetector is 15 mm. Based on the suggested suitable sensor-detector separation distance for measuring cortical hemodynamic changes, only 30-mm channels were analyzed in this study [ 25 ].

For our study, we used the 48-channel configuration ( Figure 1 ). The detected light signals in each wavelength were filtered with a band-pass filter (0.00 Hz-0.1 Hz) to reduce the effect of environmental noise–related light and body movements. In addition, channels with low-quality information (signal-to-noise ratio <30 dB) were removed from the hemodynamic analysis. The accumulated oxygenated hemoglobin (accHbO 2 ) values in the resting state represent the activation of the prefrontal cortex. In accordance with the theory that oxygenated hemoglobin has superior sensitivity and signal-to-noise ratio compared to deoxygenated hemoglobin data, only oxygenated hemoglobin were used for this analysis [ 26 - 28 ].

The means and SDs for accHbO 2 were calculated from regions of interest (ROIs) in the right and left dorsolateral prefrontal cortices (DLPFCs), right and left ventrolateral prefrontal cortices (VLPFCs), right and left frontopolar cortices (FPCs), and right and left orbitofrontal cortices (OFCs), based on Brodmann area 46. The right and left DLPFCs comprise channels 1, 2, 3, 5, 6, 11, 17, and 18 and channels 19, 20, 33, 34, 35, 38, 39, and 43, respectively. The right and left VLPFCs comprise channels 4, 9, and 10 and channels 40, 44, and 45, respectively. The right and left FPCs comprise channels 7, 8, 12, 13, 21, 22, 25, and 26 and channels 23, 24, 27, 28, 36, 37, 41, and 42, respectively. The right and left OFCs comprise channels 14, 15, 16, 29, and 30 and channels 31, 32, 46, 47, and 48, respectively ( Figure 1 ).

Digital App for Panic Disorder

The app for panic disorder is a smartphone-based app for treatment of clinical symptoms of panic disorder. The mobile app has 4 categories: diary, education, quest, and serious games. The diary category has three items: (1) assessment of daily psychological status, including mood and anxiety; (2) assessment of panic symptoms, including frequency and severity; and (3) consumption of medication, including regular medication and pro re nata medications. The education category has three items: (1) knowledge about panic disorders, (2) knowledge about medications for panic disorder, and (3) knowledge about panic disorder treatment, including CBT, breathing therapy, and positive thinking therapy. The quests include two treatments: (1) eye movement desensitization and reprocessing therapy and (2) positive thinking therapy. The serious games include two games: (1) a breathing game and (2) an exposure therapy game.

The diary, education, and serious games (ie, the breathing game and exposure therapy game) are important parts of CBT for panic disorder [ 29 - 32 ]. The efficacy of CBT for panic disorder has been examined in various randomized controlled trials [ 33 , 34 ]. Eye movement desensitization and reprocessing therapy are also known to help reduce panic symptoms [ 35 , 36 ]. We confirmed that the replacement of worry with different forms of positive ideation shows beneficial effects [ 37 ], so a similar type of positive thinking therapy can also be expected to show benefits. Multimedia Appendix 1 provides additional information on the app.

Ethical Considerations

The study trial was approved by the institutional review board of Chung-Ang University Hospital (1041078-202112-HR-349-01) and written informed consent was obtained from all participants. Participants received an explanation from the researchers that included an overview of the study and a description of the methodology and purpose before deciding to participate. Additionally, they were informed that participation was voluntary, informed about our confidentiality measures, given the option to withdraw, and informed about potential side effects and compensation. Participants in this study received ₩100,000 (US $75.50) as transportation reimbursement. Additionally, the various scales and fNIRS assessments were offered at no cost to the participants. The participants received the results of the tests in the form of a report via postal mail or email after the conclusion of the study. They also receive an explanatory document and consent form from the researchers that included contact information for any inquiries. If the participant agreed to take part in the study after understanding the consent form, the research proceeded. The participants’ personal information was not collected. Instead, a unique identifier was assigned to the collected data for the sole purpose of research management.

Study Procedure

A randomized and treatment-as-usual–controlled design was applied in this study. After screening, all participants with panic disorder were randomly assigned to the app use group or the control group. The randomization sequence in our design was generated using SPSS (version 24.0; IBM Corp), with a 1:1 allocation between groups. At baseline and after intervention, all patients with panic disorder were assessed with the PDSS for panic symptoms, the HAM-A for objective anxiety symptoms, and the GAD-7 for subjective anxiety symptoms. At baseline and after intervention, the hemodynamic response in all patients with panic disorder was assessed using NIRSIT. The app use group was asked to use the app for panic disorder 20 minutes per day, 5 times per week, for 4 weeks. The control group was asked to read short educational letters that were delivered via a social network service 5 times per week for 4 weeks. The short letters contained information about panic disorder and its treatment.

Demographic and Clinical Characteristics

After recruitment, 56 patients underwent eligibility assessments. A total of 6 individuals were excluded because they did not meet the inclusion criteria. The remaining patients were divided into 2 groups: 25 were assigned to the app use group and 21 to the control group, as 4 patients were excluded; contact was suddenly lost with 1 patient contact and 1 dropped out for personal reasons. In addition, 2 patients in the control group quit the study after reporting poor benefits from the short educational letters. Therefore, 25 people in the app use group and 21 people in the control group were analyzed. Figure 2 shows the Consolidated Standards of Reporting Trials (CONSORT) flowchart for participant flow through the trial.

There were no significant differences in age, sex ratio, years of education, marital status, employment status, or substance habits, including smoking and alcohol use, between the app use group and the control group ( Table 1 ).

b Chi-square.

There were no significant differences in HAM-A score, GAD-7 score, or PDSS score at baseline between the app use group and control group ( Table 1 ).

Comparison of Changes in Clinical Scales Between App Use Group and Control Group

The number of participants with improved panic symptoms in the app use group (20/25, 80%) was greater than in the control group (6/21, 29%; χ 2 1 =12.3; P =.005).

During the treatment period, the app use group showed greater improvement in PDSS score than the control group ( F 1,44 =7.03; P =.01). In the app use group, the PDSS score decreased by 42.5% (mean score 14.3, SD 6.5 at baseline and mean score 7.2, SD 3.6 after the intervention), while the score decreased by 14.6% in the control group (mean score 12.4, SD 5.2 at baseline and mean score 9.8, SD 7.9 after intervention) ( Figure 3 ).

During the treatment period, there were no significant differences in the change in HAM-A scores ( F 1,44 =2.83; P =.09) and GAD-7 scores ( F 1,44 =0.22; P =.64) between the app use group and control group ( Figure 3 ).

Comparison of Changes in accHbO 2 Values Between App Use Group and Control Group

There were no significant differences in accHbO 2 in the right (t 45 =0.84; P =.40) or left (t 45 =0.73; P =.46) DLPFCs, right (t 45 =1.04; P =.31) or left (t 45 =0.88; P =.39) VLPFCs, right (t 45 =-0.18; P =.86) or left (t 45 =1.85; P =.07) FPCs, or right (t 45 =0.33; P =.74) or left (t 45 =1.89; P =.07) OFCs in the app use and control groups at baseline.

During the treatment period, the app use group showed a greater reduction in accHbO 2 in the right VLPFC ( F 1,44 =8.22; P =.006) and right OFC ( F 1,44 =8.88; P =.005) compared to the control group ( Figure 1 ). During the treatment period, there were no significant differences in the change in accHbO 2 in the other ROIs between the app use and control groups.

Correlations Between the Changes in PDSS Scores and the Changes in accHbO 2

In all participants (ie, the app use group plus the control group), there was a positive correlation between the change in PDSS score and the change in accHbO 2 in the right VLPFC ( r =0.44; P =.002). In the app use group, there was a positive correlation between the change in PDSS score and the changes in accHbO 2 in the right VLPFC ( r =0.42; P =.04). However, in the control group, there was no significant correlation between the change in PDSS score and the change in accHbO 2 in the right VLPFC ( r =0.22; P =.16).

In all participants, there was a positive correlation between the change in PDSS score and the change in accHbO 2 in the right OFC ( r =0.44; P =.002). In both the app use group ( r =0.34; P =.09) and control group ( r =0.33; P =.13), there was no significant correlation between the change in PDSS score and the change in accHbO 2 in the right OFC ( Figure 4 ).

Principal Findings

This study showed that a digital app was effective for symptom reduction, as well as decreasing brain activity in the VLPFCs and OFCs, in patients with panic disorder. In addition, the panic disorder symptom improvement was positively correlated with decreased brain activity in the VLPFCs and OFCs in the resting state.

The digital app used in this trial proved to be effective in reducing panic symptoms when compared to the control group, as demonstrated by the reduction in the PDSS score. We believe that this is due to the combined effect of the 4 parts of the program, namely the diary, education, quest, and serious games. The diary component helps identify and correct faulty perceptions and enables cognitive reconstruction. The education component provides information about the nature and physiology of panic disorder. The breathing game helps the participant return to a relaxed condition, while the exposure therapy game allows the participant to experience agoraphobic situations in a safe environment, which helps cognitive restructuring. These are the important parts of CBT for panic disorder and have shown efficacy, as reported earlier [ 29 - 32 ]. The control group also received educational data, including the importance of keeping a diary of one’s panic symptoms and how to do it, as well as self-guided direction on breathing exercises, but failed to show a significant reduction of symptoms compared to the app use group. We think this is due to lack of proper feedback in the control group. The app shows real-time feedback on breathing exercises using breathing sounds, and a message was sent if the user of the program failed to use the program for more than 2 days. We know that the therapeutic effect is better when immediate feedback is provided to patients undergoing CBT treatment [ 38 ]. Therefore, we think that the decrease in PDSS score was smaller because the control group did not receive feedback from the app.

The control group also received educational data on diary recording, panic disorder information, and how to execute breathing therapy and exposure therapy. We measured their reduction in the PDSS score, but we found it was less than in the app use group due to a lack of proper daily management.

However, the app failed to lead to a difference in the reduction in anxiety, as defined by the HAM-A and GAD-7 scales, between the 2 groups. This is most likely due to a lack of power, as the trial was conducted as a pilot study. Other studies using CBT techniques or serious games have demonstrated reductions in anxiety symptoms in patients with panic disorder [ 14 ]. Likewise, this study showed a trend toward a reduction in anxiety symptoms, although this was not statistically significant, and future research with more participants may show that these kinds of programs are also effective in controlling anxiety.

Two major changes in brain activity were noted in the app use group, namely reductions in VLPFC and OFC activation. The functions of the OFC are varied and include control of inappropriate behavior and emotional responses, decision-making, and solving problems [ 39 , 40 ]. Abnormalities in the function of the OFC can cause problems in dealing with anxiety and show that it is deeply involved in the increasing the sense of fear in the fear response [ 17 ]. The results of this study confirm that OFC activity decreases as treatment progresses. This reinforces the results of a previous study, which showed that patients with panic disorder had increased OFC activity and that when the panic disorder was treated, the activity of the OFC was reduced, as indicated by decreased cerebral glucose metabolic rates [ 17 , 41 ].

The VLPFC is known to be associated with the amygdala and to maintain flexible attention and responses to environmental threats [ 42 , 43 ]. The amygdala is the backbone of the fear network, and the VLPFC is also known to be deeply involved in the processing of fear [ 43 - 45 ]. Several studies have shown increased activity in patients with panic disorder in the inferior frontal gyrus, which envelops the VLPFC, and other related regions, including the prefrontal cortex, hippocampus, and OFC [ 16 , 46 , 47 ]. After panic disorder treatment, such as with CBT, decreased amygdala and inferior frontal gyrus activation in fear situations was confirmed [ 48 , 49 ]. Through panic disorder treatment, inferior frontal gyrus activation decreased to a normal level; this happened because the treatment reduced fear cognition related to harm expectancy or attention to threats [ 49 - 51 ]. We consider that VLPFC activation increases to modulate the amygdala and decreases with treatment for panic disorder.

We believe that these reductions of brain activity in the VLPFC and OFC reflect how the app affected the patients. We know that overprediction of fear or panic is an important feature of anxiety disorders [ 52 ]. The app for panic disorder, including diary, education, quest, and serious game components, allowed users to correct their faulty perceptions about fear. As mentioned earlier, the VLPFC and OFC are related to fear management, so we can expect that activity of the VLPFC and OFC will be reduced through repeated app use as users learn how to deal with fear.

Limitations

This study has the following limitations: Most of the patients were effectively treated with alprazolam or other anxiolytics, such as SSRIs. Thus, treatment with antianxiety drugs may have influenced our results. Moreover, this study assessed changes immediately after app use. A long-term follow-up to evaluate the sustainability of the observed improvements would provide valuable insights into the effectiveness of the intervention over time. App use time could be easily tracked for the app use group; however, it was challenging to independently monitor the time the control group spent reading educational materials. Due to the limitations of available research tools, no investigation has been conducted on deep brain structures such as the amygdala, which is most closely related to panic disorders.

Conclusions

We believe that this app for panic disorder effectively reduces symptoms and noticeably impacts brain activity in specific areas. We observed a positive link between improvement in panic symptoms and decreased brain activity in the VLPFCs and OFCs in a resting state. These findings support the use of targeted interventions to determine the brain’s contribution to symptom relief. Further research should explore the duration of these positive effects and make digital therapy accessible to more individuals, thus unlocking its full potential in mental health care.

Data Availability

The data sets generated and analyzed during this study are not publicly available as they contain information that could compromise the privacy and consent of the research participants. However, the transformed data are available upon reasonable request from the authors.

Conflicts of Interest

None declared.

Digital app for panic disorder.

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Abbreviations

Edited by A Mavragani; submitted 03.08.23; peer-reviewed by M Aksoy; comments to author 01.09.23; revised version received 11.09.23; accepted 08.03.24; published 12.04.24.

©KunJung Kim, Hyunchan Hwang, Sujin Bae, Sun Mi Kim, Doug Hyun Han. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.04.2024.

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The role of perceived motivation and workers’ productivity within educational sectors in cross river state, nigeria, glory emmanuel edoho, ojong a, rose, fidelis ashiekong ukpanukpong, elizabeth odije patrick.

The thrust of this paper is to unravel the role of perceived motivation and workers’ productivity in educational sectors. By its objectives, It will show the importance of motivation to employees, It will reveal the strategies likely to motivate employees for better work results. To achieve the purpose of this study, one research question was posed, and one null hypothesis was generated. Survey research design was adopted for the study. A sample of seven hundred and ninety two (792) respondents out of eight thousand one hundred and twelve (8112) administrative staff being the population was randomly selected from this study. The selection was done through the stratified random sampling technique. Questionnaire was the main instrument used for data collection. The instrument was subjected to face validation. The reliability estimate of the instrument was established using the Cronbach Alpha reliability method. Pearson Product Moment Correlation Analysis was the statistical analysis techniques adopted in the study. The hypothesis was subjected to testing at 0.05 level of significance with relative degrees of freedom. The results of the findings revealed that, there is a positive relationship between medical services as motivational factor and workers’ productivity. Based on the findings of the study, it was recommended among others that management of all educational sectors should ensure that they build a healthy public policy, zero bills, subsided medical services as motivational package for efficient and productivity.

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13 Different Types of Hypothesis (2024)
The Null Hypothesis and Research Hypothesis
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Null Hypothesis
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Null & Alternative Hypotheses
Tip Be careful with your words when you report the results of a statistical test in a research paper or thesis. If you reject the null hypothesis, you can say that the alternative hypothesis is supported. On the other hand, if you fail to reject the null hypothesis, then you can say that the alternative hypothesis is not supported. Never say ...
How to Write a Null Hypothesis (5 Examples)
Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms: H0 (Null Hypothesis): Population parameter =, ≤, ≥ some value. HA (Alternative Hypothesis): Population parameter <, >, ≠ some value. Note that the null hypothesis always contains the equal sign.
How to Write a Null Hypothesis (with Examples and Templates)
Write a research null hypothesis as a statement that the studied variables have no relationship to each other, or that there's no difference between 2 groups. Write a statistical null hypothesis as a mathematical equation, such as. μ 1 = μ 2 {\displaystyle \mu _ {1}=\mu _ {2}} if you're comparing group means.
How to Write a Strong Hypothesis
5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
Null Hypothesis: Definition, Rejecting & Examples
When your sample contains sufficient evidence, you can reject the null and conclude that the effect is statistically significant. Statisticians often denote the null hypothesis as H 0 or H A.. Null Hypothesis H 0: No effect exists in the population.; Alternative Hypothesis H A: The effect exists in the population.; In every study or experiment, researchers assess an effect or relationship.
5.2
5.2 - Writing Hypotheses. The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis ( H 0) and an alternative hypothesis ( H a ). Null Hypothesis. The statement that there is not a difference in the population (s), denoted as H 0.
Null and Alternative Hypotheses
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.". On the other hand, the alternative hypothesis (H A) answers "Yes, there ...
Null Hypothesis Definition and Examples, How to State
Step 1: Figure out the hypothesis from the problem. The hypothesis is usually hidden in a word problem, and is sometimes a statement of what you expect to happen in the experiment. The hypothesis in the above question is "I expect the average recovery period to be greater than 8.2 weeks.". Step 2: Convert the hypothesis to math.
How to Write a Strong Hypothesis
Step 5: Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
Null Hypothesis
Definition. In formal hypothesis testing, the null hypothesis ( H0) is the hypothesis assumed to be true in the population and which gives rise to the sampling distribution of the test statistic in question (Hays 1994 ). The critical feature of the null hypothesis across hypothesis testing frameworks is that it is stated with enough precision ...
7.3: The Research Hypothesis and the Null Hypothesis
This null hypothesis can be written as: H0: X¯ = μ H 0: X ¯ = μ. For most of this textbook, the null hypothesis is that the means of the two groups are similar. Much later, the null hypothesis will be that there is no relationship between the two groups. Either way, remember that a null hypothesis is always saying that nothing is different.
Research Hypothesis: Definition, Types, Examples and Quick Tips
3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
10.1
10.1. 10.1 - Setting the Hypotheses: Examples. A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or ...
Addressing the Null & Alternate Hypotheses
When addressing the null and alternate hypothesis in the Discussion: State whether the confidence intervals overlap with the control (be specific about which treatment (s) overlap). If you reject or fail to reject the null hypothesis (use this language). A full restatement of the supported hypothesis. Click on the hotspots below to learn about ...
What Is The Null Hypothesis & When To Reject It
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 ...
Null Hypothesis Examples
An example of the null hypothesis is that light color has no effect on plant growth. The null hypothesis (H 0) is the hypothesis that states there is no statistical difference between two sample sets. In other words, it assumes the independent variable does not have an effect on the dependent variable in a scientific experiment.
An Introduction to Statistics: Understanding Hypothesis Testing and
HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ...
Hypothesis Examples: How to Write a Great Research Hypothesis
Simple hypothesis: This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.; Complex hypothesis: This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.; Null hypothesis: This hypothesis suggests no relationship exists between two or more variables.
What is Null Hypothesis? What Is Its Importance in Research?
Scientists begin their research with a hypothesis that a relationship of some kind exists between variables. The null hypothesis is the opposite stating that no such relationship exists. Null hypothesis may seem unexciting, but it is a very important aspect of research. In this article, we discuss what null hypothesis is, how to make use of it ...
How To Write A Hypothesis In A Research Paper
Step 3: Formulate a Clear Statement. Precision is the key to shaping a concise and strong hypothesis. To create a well-structured hypothesis, condense your thoughts into a single, easy-to-follow sentence. Also, do not forget to clearly express the expected connection between your independent and dependent variables.
The Null Hypothesis
The null hypothesis, as described by Anthony Greenwald in 'Consequences of Prejudice Against the Null Hypothesis,' is the hypothesis of no difference between treatment effects or of no association between variables. Unfortunately in academia, the 'null' is often associated with 'insignificant,' 'no value,' or 'invalid.'.
How to Find P Value from a Test Statistic
Hypothesis tests are used to test the validity of a claim that is made about a population. This claim that's on trial, in essence, is called the null hypothesis (H 0).The alternative hypothesis (H a) is the one you would believe if the null hypothesis is concluded to be untrue.Learning how to find the p-value in statistics is a fundamental skill in testing, helping you weigh the evidence ...
Anger is eliminated with the disposal of a paper written ...
A BF favouring the alternative over the null hypothesis (BF 10) offers strong evidence for the alternative hypothesis when it is over 10. Values less than 0.33 indicate support for the null ...
Journal of Medical Internet Research
Background: Panic disorder is a common and important disease in clinical practice that decreases individual productivity and increases health care use. Treatments comprise medication and cognitive behavioral therapy. However, adverse medication effects and poor treatment compliance mean new therapeutic models are needed. Objective: We hypothesized that digital therapy for panic disorder may ...
Lwati: A Journal of Contemporary Research
The thrust of this paper is to unravel the role of perceived motivation and workers' productivity in educational sectors. By its objectives, It will show the importance of motivation to employees, It will reveal the strategies likely to motivate employees for better work results. To achieve the purpose of this study, one research question was posed, and one null hypothesis was generated.

How to Write a Null Hypothesis (5 Examples)

Example 1: Weight of Turtles

Example 2: Height of Males

Example 3: Graduation Rates

Example 4: Burger Weights

Example 5: Citizen Support

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Writing Null Hypotheses in Research and Statistics

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Examples of Null Hypotheses

Null Hypothesis vs. Alternative Hypothesis

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How to Write a Strong Hypothesis | Steps & Examples

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Variables in hypotheses

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Step 2. Do some preliminary research

Step 3. Formulate your hypothesis

4. Refine your hypothesis

5. Phrase your hypothesis in three ways

6. Write a null hypothesis

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How to Write a Strong Hypothesis | Guide & Examples

Table of contents

Variables in hypotheses

Prevent plagiarism, run a free check.

Step 2: Do some preliminary research

Step 3: Formulate your hypothesis

Step 4: Refine your hypothesis

Step 5: Phrase your hypothesis in three ways

Step 6. Write a null hypothesis

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Shona McCombes

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7.3: The Research Hypothesis and the Null Hypothesis

The Research Hypothesis

Directional Research Hypothesis

Non-Directional Research Hypothesis

The Null Hypothesis

Contributors and Attributions

The Craft of Writing a Strong Hypothesis

Table of Contents

What is a Hypothesis?

Different Types of Hypotheses‌

1. Null hypothesis

2. Alternative hypothesis

3. Simple hypothesis

4. Complex hypothesis

5. Associative and casual hypothesis

6. Empirical hypothesis

7. Statistical hypothesis