hypothesis generator

Hypothesis Maker Online

Looking for a hypothesis maker? This online tool for students will help you formulate a beautiful hypothesis quickly, efficiently, and for free.

Are you looking for an effective hypothesis maker online? Worry no more; try our online tool for students and formulate your hypothesis within no time.

• 🔎 How to Use the Tool?
• ⚗️ What Is a Hypothesis in Science?

👍 What Does a Good Hypothesis Mean?

• 🧭 Steps to Making a Good Hypothesis

🔗 References

📄 hypothesis maker: how to use it.

Our hypothesis maker is a simple and efficient tool you can access online for free.

If you want to create a research hypothesis quickly, you should fill out the research details in the given fields on the hypothesis generator.

Below are the fields you should complete to generate your hypothesis:

• Who or what is your research based on? For instance, the subject can be research group 1.
• What does the subject (research group 1) do?
• What does the subject affect? - This shows the predicted outcome, which is the object.
• Who or what will be compared with research group 1? (research group 2).

Once you fill the in the fields, you can click the ‘Make a hypothesis’ tab and get your results.

⚗️ What Is a Hypothesis in the Scientific Method?

A hypothesis is a statement describing an expectation or prediction of your research through observation.

It is similar to academic speculation and reasoning that discloses the outcome of your scientific test . An effective hypothesis, therefore, should be crafted carefully and with precision.

A good hypothesis should have dependent and independent variables . These variables are the elements you will test in your research method – it can be a concept, an event, or an object as long as it is observable.

You can observe the dependent variables while the independent variables keep changing during the experiment.

In a nutshell, a hypothesis directs and organizes the research methods you will use, forming a large section of research paper writing.

Hypothesis vs. Theory

A hypothesis is a realistic expectation that researchers make before any investigation. It is formulated and tested to prove whether the statement is true. A theory, on the other hand, is a factual principle supported by evidence. Thus, a theory is more fact-backed compared to a hypothesis.

Another difference is that a hypothesis is presented as a single statement , while a theory can be an assortment of things . Hypotheses are based on future possibilities toward a specific projection, but the results are uncertain. Theories are verified with undisputable results because of proper substantiation.

When it comes to data, a hypothesis relies on limited information , while a theory is established on an extensive data set tested on various conditions.

You should observe the stated assumption to prove its accuracy.

Since hypotheses have observable variables, their outcome is usually based on a specific occurrence. Conversely, theories are grounded on a general principle involving multiple experiments and research tests.

This general principle can apply to many specific cases.

The primary purpose of formulating a hypothesis is to present a tentative prediction for researchers to explore further through tests and observations. Theories, in their turn, aim to explain plausible occurrences in the form of a scientific study.

It would help to rely on several criteria to establish a good hypothesis. Below are the parameters you should use to analyze the quality of your hypothesis.

🧭 6 Steps to Making a Good Hypothesis

Writing a hypothesis becomes way simpler if you follow a tried-and-tested algorithm. Let’s explore how you can formulate a good hypothesis in a few steps:

Step #1: Ask Questions

The first step in hypothesis creation is asking real questions about the surrounding reality.

Why do things happen as they do? What are the causes of some occurrences?

Your curiosity will trigger great questions that you can use to formulate a stellar hypothesis. So, ensure you pick a research topic of interest to scrutinize the world’s phenomena, processes, and events.

Step #2: Do Initial Research

Carry out preliminary research and gather essential background information about your topic of choice.

The extent of the information you collect will depend on what you want to prove.

Your initial research can be complete with a few academic books or a simple Internet search for quick answers with relevant statistics.

Still, keep in mind that in this phase, it is too early to prove or disapprove of your hypothesis.

Step #3: Identify Your Variables

Now that you have a basic understanding of the topic, choose the dependent and independent variables.

Take note that independent variables are the ones you can’t control, so understand the limitations of your test before settling on a final hypothesis.

Step #4: Formulate Your Hypothesis

You can write your hypothesis as an ‘if – then’ expression . Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test.

For instance: If I study every day, then I will get good grades.

Step #5: Gather Relevant Data

Once you have identified your variables and formulated the hypothesis, you can start the experiment. Remember, the conclusion you make will be a proof or rebuttal of your initial assumption.

So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement.

Step #6: Record Your Findings

Finally, write down your conclusions in a research paper .

Outline in detail whether the test has proved or disproved your hypothesis.

Edit and proofread your work, using a plagiarism checker to ensure the authenticity of your text.

We hope that the above tips will be useful for you. Note that if you need to conduct business analysis, you can use the free templates we’ve prepared: SWOT , PESTLE , VRIO , SOAR , and Porter’s 5 Forces .

❓ Hypothesis Formulator FAQ

Updated: Oct 25th, 2023

• How to Write a Hypothesis in 6 Steps - Grammarly
• Forming a Good Hypothesis for Scientific Research
• The Hypothesis in Science Writing
• Scientific Method: Step 3: HYPOTHESIS - Subject Guides
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Use our hypothesis maker whenever you need to formulate a hypothesis for your study. We offer a very simple tool where you just need to provide basic info about your variables, subjects, and predicted outcomes. The rest is on us. Get a perfect hypothesis in no time!

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Create your rock solid experiment hypothesis

A. fill out the form  , b. your hypothesis will appear here, why should you use this method.

Hypotheses give good test results, simple as that. Use our tool to get structure in how to formulate your hypotheses.

You could use it as a kind of "bullshit detector" - if your hypothesis doesn’t fit into the template it's probably not a good testing hypothesis.

A good hypothesis is a multi-stage rocket - IAR

• Insights - What have you noticed that makes you think that you have to make a change?
• Action - What will you do?
• Results - What do you want to accomplish and how do you measure it?

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♥ from the Conversionista! team | Report a bug or an issue

Use Our Free A/B Testing Hypothesis Generator . Never Miss Key Elements From Your Hypotheses. Get Big Conversion Lifts.

Observation, inadvertent impact.

Streamline Your Hypothesis Generation Research with Custom Templates the Pros Use.

Have questions about a/b testing hypotheses, what is a hypothesis.

Many people define a hypothesis as an “educated guess”.

To be more precise, a properly constructed hypothesis predicts a possible outcome to an experiment or a test where one variable (the independent one ) is tweaked and/or modified and the impact is measured by the change in behavior of another variable (generally the dependent one).

A hypothesis should be specific (it should clearly define what is being altered and what is the expected impact), data-driven (the changes being made to the independent variable should be based on historic data or theories that have been proven in the past), and testable (it should be possible to conduct the proposed test in a controlled environment to establish the relationship between the variables involved, and disprove the hypothesis - should it be untrue.)

What is the Cost of a Hastily Assembled Hypothesis?

According to an analysis of over 28,000 tests run using the Convert Experiences platform, only 1 in 5 tests proves to be statistically significant.

While more and more debate is opening up around sticking to the concept of 95% statistical significance, it is still a valid rule of thumb for optimizers who do not want to get into the fray with peeking vs. no peeking, and custom stopping rules for experiments.

There might be a multitude of reasons why a test does not reach statistical significance. But framing a tenable hypothesis that already proves itself logistically feasible on paper is a better starting point than a hastily assembled assumption.

Moreover, the aim of an A/B test may be to extract a learning, but some learnings come with heavy costs. 26% decrease in conversion rates to be specific.

A robust hypothesis may not be the answer to all testing woes, but it does help prioritisation of possible solutions and leads testing teams to pick low hanging fruits.

How is an A/B Testing Hypothesis Different?

An A/B test should be treated with the same rigour as tests conducted in laboratories. That is an easy way to guarantee better hypotheses, more relevant experiments, and ultimately more profitable optimization programs.

The focus of an A/B test should be on first extracting a learning , and then monetizing it in the form of increased registration completions, better cart conversions and more revenue.

If that is true, then an A/B test hypothesis is not very different from a regular scientific hypothesis. With a couple of interesting points to note:

• Most scientific hypotheses proceed with one independent variable and one dependent variable, for the sake of simplicity. But in A/B tests, there might be changes made to several independent variables at the same time. Under such circumstances it is good to explore the relationship between the independent variables to make sure that they do not inadvertently impact one another. For example changing both the value proposition and button copy of a landing page to determine improvement in click through or completion rates is tricky. Reaching a point where the browser is compelled to click the button could easily have been impacted by the value proposition (as in a strong hook and heading). So what caused the improvement in the dependent variable? Was it the change to the first element or the second one?
• The concept of Operational Definition is non-negotiable in most laboratory experiments. And comes baked with the question of ethics or morality. Operation Definition is the specific process that will be used to quantify the change in the value/behavior of the independent variable in the test. As an example, if a test wishes to measure the level of frustration that subjects experience when they are exposed to certain stimuli, researchers must be careful to define exactly how they will measure the output or frustration. Should they allow the test subjects to act out, in which case they may hurt or harm other individuals. Or should they use a non-invasive technique like an fMRI scan to monitor brain activity and collect the needed data. In A/B tests however, since data is collected through relatively inanimate channels like analytics dashboards, generally little thought is spared to Operational Definition and the impact of A/B testing on the human subjects (site traffic in this case).

The 5 Essential Parts of an A/B Testing Hypothesis

A robust A/B testing hypothesis should be assembled in 5 key parts:

1. OBSERVATION

This includes a clear outline of the problem (the unexplained phenomenon) observed and what it entails. This section should be completely free of conjecture and rely solely on good quality data - either qualitative and/or quantitative - to bring a potential area of improvement to light. It also includes a mention of the way in which the data is collected.

Proper observation ensures a credible hypothesis that is easy to “defend” later down the line.

2. EXECUTION

This is the where, what, and the who of the A/B test. It specifies the change(s) you will be making to site element(s) in an attempt to solve the problem that has been outlined under “OBSERVATION”. It serves to also clearly define the segment of site traffic that will be exposed to the experiment.

Proper execution guidelines set the rhythm for the A/B test. They define how easy or difficult it will be to deploy the test and thus aid hypothesis prioritization .

This is where you make your educated guess or informed prediction. Based on a diligently identified OBSERVATION and EXECUTION guidelines that are possible to deploy, your OUTCOME should clearly mention two things:

• The change (increase or decrease) you expect to see to the problem or the symptoms of the problem identified under OBSERVATION.
• The Key Performance Indicators (KPIs) you will be monitoring to gauge whether your prediction has panned out, or not.

In general most A/B tests have one primary KPI and a couple of secondary KPIs or ways to measure impact. This is to ensure that external influences do not skew A/B test results and even if the primary KPI is compromised in some way, the secondary KPIs do a good job of indicating that the change is indeed due to the implementation of the EXECUTION guidelines, and not the result of unmonitored external factors.

4. LOGISTICS

An important part of hypothesis formulation, LOGISTICS talk about what it will take to collect enough clean data from which a reliable conclusion can be drawn. How many unique tested visitors, what is the statistical significance desired, how many conversions is enough and what is the duration for which the A/B test should run? Each question on its own merits a blog or a lesson. But for the sake of convenience, Convert has created a Free Sample Size & A/B/N Test Duration Calculator .

Set the right logistical expectations so that you can prioritise your hypotheses for maximum impact and minimum effort .

5. INADVERTENT IMPACT

This is a nod in the direction of ethics in A/B testing and marketing, because experiments involve humans and optimizers should be aware of the possible impact on their behavior.

Often a thorough analysis at this stage can modify the way impact is measured or an experiment is conducted. Or Convert certainly hopes that this will be the case in future. Here’s why ethics do matter in testing.

Now Organize, Prioritise & Learn from Your Hypotheses.

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Research Hypothesis Generator

Generate research hypotheses with ai.

• Academic Research: Formulate a hypothesis for your thesis or dissertation based on your research topic and objectives.
• Data Analysis: Generate a hypothesis to guide your data collection and analysis strategy.
• Market Research: Develop a hypothesis to guide your investigation into market trends and consumer behavior.
• Scientific Research: Create a hypothesis to direct your experimental design and data interpretation.

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Online Hypothesis Generator

Add the required information into the fields below to build a list of well-formulated hypotheses.

• If patients follow medical prescriptions, then their condition will improve.
• If patients follow medical prescriptions, then their condition will show better results.
• If patients follow medical prescriptions, then their condition will show better results than those who do not follow medical prescriptions.
• H0 (null hypothesis) - Attending most lectures by first-year students has no effect on their exam scores.
• H1 (alternative hypothesis) - Attending most lectures by first-year students has a positive effect on their exam scores.

* Hint - choose either null or alternative hypothesis

⭐️ Hypothesis Creator: the Benefits

• 🔎 How to Use the Tool?
• 🤔 What Is a Hypothesis?
• 👣 Steps to Generating a Hypothesis
• 🔍 References

🔎 Hypothesis Generator: How to Use It?

The generation of a workable hypothesis is not an easy task for many students. You need to research widely, understand the gaps in your study area, and comprehend the method of hypothesis formulation to the dot. Lucky for you, we have a handy hypothesis generator that takes hours of tedious work out of your study process.

To use our hypothesis generator, you’ll need to do the following:

• Indicate your experimental group (people, phenomena, event)
• Stipulate what it does
• Add the effect that the subject’s activities produce
• Specify the comparison group

Once you put all this data into our online hypothesis generator, click on the “Generate hypothesis” tab and enjoy instant results. The tool will come up with a well-formulated hypothesis in seconds.

🤔 What Is a Research Hypothesis?

A hypothesis is a claim or statement you make about the assumed relationship between the dependent and independent variables you're planning to test. It is formulated at the beginning of your study to show the direction you will take in the analysis of your subject of interest.

The hypothesis works in tandem with your research purpose and research question , delineating your entire perspective.

For example, if you focus on the quality of palliative care in the USA , your perspective may be as follows.

This way, your hypothesis serves as a tentative answer to your research question, which you aim to prove or disprove with scientific data, statistics, and analysis.

Hypothesis Types

In most scholarly studies, you’ll be required to write hypotheses in pairs – as a null and alternative hypothesis :

• The alternative hypothesis assumes a statistically significant relationship between the identified variables. Thus, if you find that relationship in the analysis process, you can consider the alternative hypothesis proven.
• A null hypothesis is the opposite; it assumes that there is no relationship between the variables. Thus, if you find no statistically significant association, the null hypothesis is considered proven.

A handy example is as follows:

You are researching the impact of sugar intake on child obesity . So, based on your data, you can either find that the number of sugar spoons a day directly impacts obesity or that the sugar intake is not associated with obesity in your sample. The hypotheses for this study would be as follows:

ALTERNATIVE

There is a relationship between the number of sugar spoons consumed daily and obesity in U.S. preschoolers.

There is no relationship between the number of sugar spoons consumed daily and obesity in U.S. preschoolers.

Besides, hypotheses can be directional and non-directional by type:

• A directional hypothesis assumes a cause-and-effect relationship between variables, clearly designating the assumed difference in study groups or parameters.
• A non-directional hypothesis , in turn, only assumes a relationship or difference without a clear estimate of its direction.

NON-DIRECTIONAL

Students in high school and college perform differently on critical thinking tests.

DIRECTIONAL

College students perform better on critical thinking tests that high-school students.

👣 How to Make a Hypothesis in Research

Now let’s cover the algorithm of hypothesis generation to make this process simple and manageable for you.

Step #1: Formulate Your Research Question

The first step is to create a research question . Study the topic of interest and clarify what aspect you're fascinated about, wishing to learn more about the hidden connections, effects, and relationships.

Step #2: Research the Topic

Next, you should conduct some research to test your assumption and see whether there’s enough published evidence to back up your point. You should find credible sources that discuss the concepts you’ve singled out for the study and delineate a relationship between them. Once you identify a reasonable body of research, it’s time to go on.

Step #3: Make an Assumption

With some scholarly data, you should now be better positioned to make a researchable assumption.

For instance, if you find out that many scholars associate heavy social media use with a feeling of loneliness, you can hypothesize that the hours spent on social networks will directly correlate with perceived loneliness intensity.

Step #4: Improve Your Hypothesis

Now that you have a hypothesis, it’s time to refine it by adding context and population specifics. Who will you study? What social network will you focus on? In this example, you can focus on the student sample’s use of Instagram .

Step #5: Try Different Phrasing

The final step is the proper presentation of your hypothesis. You can try several variants, focusing on the variables, correlations , or groups you compare.

For instance, you can say that students spending 3+ hours on Instagram every day are lonelier than their peers. Otherwise, you can hypothesize that heavy social media use leads to elevated feelings of loneliness.

👀 Null Hypothesis Examples

If you’re unsure about how to generate great hypotheses, get some inspiration from the list of examples formulated by our writing pros.

Thank you for reading this article! If you’re planning to analyze business issues, try our free templates: PEST , PESTEL , SWOT , SOAR , VRIO , and Five Forces .

❓ Hypothesis Generator FAQ

❓ what does hypothesis mean.

A hypothesis in an essay or a larger research assignment is your claim or prediction of the relationship you assume between the identified dependent and independent variables. You share an assumption that you’re going to test with research and data analysis in the later sections of your paper.

❓ How to create a hypothesis?

The first step to formulating a good hypothesis is to ask a question about your subject of interest and understand what effects it may experience from external sources or how it changes over time. You can identify differences between groups and inquire into the nature of those distinctions. In any way, you need to voice some assumption that you’ll further test with data; that assumption will be your hypothesis for a study.

❓ What is a null and alternative hypothesis?

You need to formulate a null and alternative hypothesis if you plan to test some relationship between variables with statistical instruments. For example, you might compare a group of students on an emotional intelligence scale to determine whether first-year students are less emotionally competent than graduates. In this case, your alternative hypothesis would state that they are, and a null hypothesis would say that there is no difference between student groups.

❓ What does it mean to reject the null hypothesis?

A null hypothesis assumes that there is no difference between groups or that the dependent variables don't have any sizable impact on the independent variable. If your null hypothesis gets rejected, it means that your alternative hypothesis has been proved, showing that there is a tangible difference or relationship between your variables.

🔗 References

• How to Write a Hypothesis in 6 Steps - Grammarly
• The Hypothesis in Science Writing
• Hypothesis Definition & Examples - Simply Psychology
• Hypothesis Examples: Different Types in Science and Research
• Forming a Good Hypothesis for Scientific Research

• Pricing Lightweight Script Blog
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AI Hypothesis Generator

Hypothesis Generator to help you come up with a boilerplate hypothesis for your test ideas. Generate well-structured hypothesis in under 10 seconds!

1. Give us a brief about your hypothesis...

Hypotheses in A/B Testing

Hypotheses form an integral part of A/B Testing. They provide a clear path and expected outcome for the test, based on the initial conditions, such as the user interface and user experience, among others. A well-defined hypothesis is the foundation of any successful A/B test, guiding the direction of the test and serving as a benchmark against which the test’s results are evaluated.

What are the benefits?

The Automated Hypothesis Creator simplifies the first step in the A/B testing process and provides several benefits:

• Quick and efficient hypothesis generation.
• Saves time and resources which can often be invested in analysing the output of the A/B test.
• Provides insightful and scientifically-backed predictions.
• Outlines a clear picture for the A/B test, thus leading to more accurate outcomes.

How to Use it with A/B Testing?

To use the Automated Hypothesis Creator with A/B testing, follow these simple steps:

• Begin by clearly formulating your query.
• Use the text area in the tool to provide the necessary input data.
• Click the “Create Hypothesis” button.
• Wait for a while for the tool to process your request and generate a hypothesis.
• Once the hypothesis is created, use it as a basis for your A/B test.

Try other free tools:

• A/B Test Headline Generator
• Sample Size Calculator
• A/B Test Duration Calculator
• Statistical Significance Calculator

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Hypothesis Testing Calculator

Related: confidence interval calculator, type ii error.

The first step in hypothesis testing is to calculate the test statistic. The formula for the test statistic depends on whether the population standard deviation (σ) is known or unknown. If σ is known, our hypothesis test is known as a z test and we use the z distribution. If σ is unknown, our hypothesis test is known as a t test and we use the t distribution. Use of the t distribution relies on the degrees of freedom, which is equal to the sample size minus one. Furthermore, if the population standard deviation σ is unknown, the sample standard deviation s is used instead. To switch from σ known to σ unknown, click on $\boxed{\sigma}$ and select $\boxed{s}$ in the Hypothesis Testing Calculator.

Next, the test statistic is used to conduct the test using either the p-value approach or critical value approach. The particular steps taken in each approach largely depend on the form of the hypothesis test: lower tail, upper tail or two-tailed. The form can easily be identified by looking at the alternative hypothesis (H a ). If there is a less than sign in the alternative hypothesis then it is a lower tail test, greater than sign is an upper tail test and inequality is a two-tailed test. To switch from a lower tail test to an upper tail or two-tailed test, click on $\boxed{\geq}$ and select $\boxed{\leq}$ or $\boxed{=}$, respectively.

In the p-value approach, the test statistic is used to calculate a p-value. If the test is a lower tail test, the p-value is the probability of getting a value for the test statistic at least as small as the value from the sample. If the test is an upper tail test, the p-value is the probability of getting a value for the test statistic at least as large as the value from the sample. In a two-tailed test, the p-value is the probability of getting a value for the test statistic at least as unlikely as the value from the sample.

To test the hypothesis in the p-value approach, compare the p-value to the level of significance. If the p-value is less than or equal to the level of signifance, reject the null hypothesis. If the p-value is greater than the level of significance, do not reject the null hypothesis. This method remains unchanged regardless of whether it's a lower tail, upper tail or two-tailed test. To change the level of significance, click on $\boxed{.05}$. Note that if the test statistic is given, you can calculate the p-value from the test statistic by clicking on the switch symbol twice.

In the critical value approach, the level of significance ($\alpha$) is used to calculate the critical value. In a lower tail test, the critical value is the value of the test statistic providing an area of $\alpha$ in the lower tail of the sampling distribution of the test statistic. In an upper tail test, the critical value is the value of the test statistic providing an area of $\alpha$ in the upper tail of the sampling distribution of the test statistic. In a two-tailed test, the critical values are the values of the test statistic providing areas of $\alpha / 2$ in the lower and upper tail of the sampling distribution of the test statistic.

To test the hypothesis in the critical value approach, compare the critical value to the test statistic. Unlike the p-value approach, the method we use to decide whether to reject the null hypothesis depends on the form of the hypothesis test. In a lower tail test, if the test statistic is less than or equal to the critical value, reject the null hypothesis. In an upper tail test, if the test statistic is greater than or equal to the critical value, reject the null hypothesis. In a two-tailed test, if the test statistic is less than or equal the lower critical value or greater than or equal to the upper critical value, reject the null hypothesis.

When conducting a hypothesis test, there is always a chance that you come to the wrong conclusion. There are two types of errors you can make: Type I Error and Type II Error. A Type I Error is committed if you reject the null hypothesis when the null hypothesis is true. Ideally, we'd like to accept the null hypothesis when the null hypothesis is true. A Type II Error is committed if you accept the null hypothesis when the alternative hypothesis is true. Ideally, we'd like to reject the null hypothesis when the alternative hypothesis is true.

Hypothesis testing is closely related to the statistical area of confidence intervals. If the hypothesized value of the population mean is outside of the confidence interval, we can reject the null hypothesis. Confidence intervals can be found using the Confidence Interval Calculator . The calculator on this page does hypothesis tests for one population mean. Sometimes we're interest in hypothesis tests about two population means. These can be solved using the Two Population Calculator . The probability of a Type II Error can be calculated by clicking on the link at the bottom of the page.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Research Hypothesis Generator Online

• ️👍 Hypothesis Maker: the Benefits
• ️🔎 How to Use the Tool?
• ️🕵️ What Is a Research Hypothesis?
• ️⚗️ Scientific Method
• ️🔗 References

👍 Hypothesis Maker: the Benefits

Here are the key benefits of this null and alternative hypothesis generator.

🔎 Hypothesis Generator: How to Use It?

Whenever you conduct research, whether a 5-paragraph essay or a more complex assignment, you need to create a hypothesis for this study.

Clueless about how to create a good hypothesis?

No need to waste time and energy on this small portion of your writing process! You can always use our hypothesis creator to get a researchable assumption in no time.

To get a ready-made hypothesis idea, you need to:

• State the object of your study
• Specify what the object does
• Lay out the outcome of that activity
• Indicate the comparison group

Once all data is inserted into the fields, you can press the “Generate now” button and get the result from our hypothesis generator for research paper or any other academic task.

🕵️ What Is a Research Hypothesis?

A hypothesis is your assumption based on existing academic knowledge and observations of the surrounding natural world.

It also involves a healthy portion of intuition because you should arrive at an interesting, commonsense question about the phenomena or processes you observe.

The traditional formula for hypothesis generation is an “if…then” statement, reflecting its falsifiability and testability.

What do these terms mean?

• Testability means you can formulate a scientific guess and test it with data and analysis.
• Falsifiability is a related feature, allowing you to refute the hypothesis with data and show that your guess has no tangible support in real-world data.

For example, you might want to hypothesize the following:

If children are given enough free play time, their intelligence scores rise quicker.

You can test this assumption by observing and measuring two groups – children involved in much free play and those who don’t get free play time. Once the study period ends, you can measure the intelligence scores in both groups to see the difference, thus proving or disproving your hypothesis, which will be testing your hypothesis. If you find tangible differences between the two groups, your hypothesis will be proven, and if there is no difference, the hypothesis will prove false.

Null and Alternative Hypothesis

As a rule, hypotheses are presented in pairs in academic studies, as your scientific guess may be refuted or proved. Thus, you should formulate two hypotheses – a null and alternative variant of the same guess – to see which one is proved with your experiment.

The alternative hypothesis is formulated in an affirmative form, assuming a specific relationship between variables. In other words, you hypothesize that the predetermined outcome will be observed if one condition is met.

Watching films before sleep reduces the quality of sleep.

The null hypothesis is formulated in a negative form, suggesting that there is no association between the variables of your interest. For example:

Watching films before sleep doesn’t affect the quality of sleep.

⚗️ Creating a Hypothesis: the Key Steps

The development and testing of multiple hypotheses are the basis of the scientific method .

Without such inquiries, academic knowledge would never progress, and humanity would remain with a limited understanding of the natural world.

How can you contribute to the existing academic base with well-developed and rigorously planned scientific studies ? Here is an introduction to the empirical method of scientific inquiry.

Step #1: Observe the World Around You

Look around you to see what’s taking place in your academic area. If you’re a biology researcher, look into the untapped biological processes or intriguing facts that nobody has managed to explain before you.

What’s surprising or unusual in your observations? How can you approach this area of interest?

That’s the starting point of an academic journey to new knowledge.

Step #2: Ask Questions

Now that you've found a subject of interest, it's time to generate scientific research questions .

A question can be called scientific if it is well-defined, focuses on measurable dimensions, and is largely testable.

Some hints for a scientific question are:

• What effect does X produce on Y?
• What happens if the intensity of X’s impact reduces or rises?
• What is the primary cause of X?
• How is X related to Y in this group of people?
• How effective is X in the field of C?

As you can see, X is the independent variable , and Y is the dependent variable.

This principle of hypothesis formulation is vital for cases when you want to illustrate or measure the strength of one variable's effect on the other.

Step #3: Generate a Research Hypothesis

After asking the scientific question, you can hypothesize what your answer to it can be.

You don't have any data yet to answer the question confidently, but you can assume what effect you will observe during an empirical investigation.

For example, suppose your background research shows that protein consumption boosts muscle growth.

In that case, you can hypothesize that a sample group consuming much protein after physical training will exhibit better muscle growth dynamics compared to those who don’t eat protein. This way, you’re making a scientific guess based on your prior knowledge of the subject and your intuition.

Step #4: Hold an Experiment

With a hypothesis at hand, you can proceed to the empirical study for its testing. As a rule, you should have a clearly formulated methodology for proving or disproving your hypothesis before you create it. Otherwise, how can you know that it is testable? An effective hypothesis usually contains all data about the research context and the population of interest.

For example:

Marijuana consumption among U. S. college students reduces their motivation and academic achievement.

• The study sample here is college students.
• The dependent variable is motivation and academic achievement, which you can measure with any validated scale (e.g., Intrinsic Motivation Inventory).
• The inclusion criterion for the study's experimental group is marijuana use.
• The control group might be a group of marijuana non-users from the same population.
• A viable research methodology is to ask both groups to fill out the survey and compare the results.

Step #5: Analyze Your Findings

Once the study is over and you have the collected dataset, it's time to analyze the findings.

The methodology should also delineate the criteria for proving or disproving the hypothesis.

Using the previous section's example, your hypothesis is proven if the experimental group reveals lower motivational scores and has a lower GPA . If both groups' motivation and GPA scores aren't statistically different, your hypothesis is false.

Step #6: Formulate Your Conclusion

Using your study's hypothesis and outcomes, you can now generate a conclusion . If the alternative hypothesis is proven, you can conclude that marijuana use hinders students' achievement and motivation. If the null hypothesis is validated, you should report no identified relationship between low academic achievement and weed use.

Thank you for reading this article! Note that if you need to conduct a business analysis, you can try our free tools: SWOT , VRIO , SOAR , PESTEL , and Porter’s Five Forces .

❓ Research Hypothesis Generator FAQ

❓ what is a research hypothesis.

A hypothesis is a guess or assumption you make by looking at the available data from the natural world. You assume a specific relationship between variables or phenomena and formulate that supposition for further testing with experimentation and analysis.

❓ How to write a hypothesis?

To compose an effective hypothesis, you need to look at your research question and formulate a couple of ways to answer it. The available scientific data can guide you to assume your study's outcome. Thus, the hypothesis is a guess of how your research question will be answered by the end of your research.

❓ What is the difference between prediction and hypothesis?

A prediction is your forecast about the outcome of some activities or experimentation. It is a guess of what will happen if you perform some actions with a specific object or person. A hypothesis is a more in-depth inquiry into the way things are related. It is more about explaining specific mechanisms and relationships.

❓ What makes a good hypothesis?

A strong hypothesis should indicate the dependent and independent variables, specifying the relationship you assume between them. You can also strengthen your hypothesis by indicating a specific population group, an intervention period, and the context in which you'll hold the study.

Statistics Calculator

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Many tutorials with simple examples.

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DATAtab takes you by the hand in the world of statistics. When making statistical decisions, such as the choice of scale or measurement level or the selection of suitable methods, Auto-Assistants ensure that you get correct results quickly.

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With DATAtab data visualization is fun! Here you can easily create meaningful charts that optimally illustrate your results.

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Our references

Alternative to statistical software like SPSS and STATA

DATAtab was designed for ease of use and is a compelling alternative to statistical programs such as SPSS and STATA. On datatab.net, data can be statistically evaluated directly online and very easily (e.g. t-test, regression, correlation etc.). DATAtab's goal is to make the world of statistical data analysis as simple as possible, no installation and easy to use. Of course, we would also be pleased if you take a look at our second project Statisty .

Extensive tutorials

Descriptive statistics.

Here you can find out everything about location parameters and dispersion parameters and how you can describe and clearly present your data using characteristic values.

Hypothesis Test

Here you will find everything about hypothesis testing: One sample t-test , Unpaired t-test , Paired t-test and Chi-square test . You will also find tutorials for non-parametric statistical procedures such as the Mann-Whitney u-Test and Wilcoxon-Test . mann-whitney-u-test and the Wilcoxon test

The regression provides information about the influence of one or more independent variables on the dependent variable. Here are simple explanations of linear regression and logistic regression .

Correlation

Correlation analyses allow you to analyze the linear association between variables. Learn when to use Pearson correlation or Spearman rank correlation . With partial correlation , you can calculate the correlation between two variables to the exclusion of a third variable.

Partial Correlation

The partial correlation shows you the correlation between two variables to the exclusion of a third variable.

Levene Test

The Levene Test checks your data for variance equality. Thus, the levene test is used as a prerequisite test for many hypothesis tests .

The p-value is needed for every hypothesis test to be able to make a statement whether the null hypothesis is accepted or rejected.

Distributions

DATAtab provides you with tables with distributions and helpful explanations of the distribution functions. These include the Table of t-distribution and the Table of chi-squared distribution

Contingency table

With a contingency table you can get an overview of two categorical variables in the statistics.

Equivalence and non-inferiority

In an equivalence trial, the statistical test aims at showing that two treatments are not too different in characteristics and a non-inferiority trial wants to show that an experimental treatment is not worse than an established treatment.

If there is a clear cause-effect relationship between two variables, then we can speak of causality. Learn more about causality in our tutorial.

Multicollinearity

Multicollinearity is when two or more independent variables have a high correlation.

Effect size for independent t-test

Learn how to calculate the effect size for the t-test for independent samples.

Reliability analysis calculator

On DATAtab, Cohen's Kappa can be easily calculated online in the Cohen’s Kappa Calculator . there is also the Fleiss Kappa Calculator . Of course, the Cronbach's alpha can also be calculated in the Cronbach's Alpha Calculator .

Analysis of variance with repeated measurement

Repeated measures ANOVA tests whether there are statistically significant differences in three or more dependent samples.

Cite DATAtab: DATAtab Team (2024). DATAtab: Online Statistics Calculator. DATAtab e.U. Graz, Austria. URL https://datatab.net

History Hypothesis Generator

If you’re searching for a hypothesis generator, you’re in the right place! With this free online tool, you’ll easily make a hypothesis from a question or from scratch.

Need a hypothesis for your history paper? This automatic hypothesis generator will save your time and nerves! Follow these 3 steps:

• ❓ Definitions
• 💡 What’s This Tool?

🔬 How to Generate a Hypothesis from a Question?

• ✅ Characteristics
• ✍️ Examples

🔗 References

❓ hypothesis in history: definitions.

In high school or college, you might need to develop a historical hypothesis for your academic paper or any other project. In the sections below, we have explained what it means for this subject.

What Is a Hypothesis?

A hypothesis is a statement or proposed explanation for a phenomenon. For it to be scientific, researchers should be able to test it.

The words “hypothesis” and “ theory ” are often used interchangeably. However, they are not the same. In exact science, a hypothesis needs to be provable to become a theory. In the non-scientific environment, the word is used more loosely.

What about a Hypothesis in History?

A historical hypothesis consists of:

• Attitudes that demonstrate relations between variables.

It is a proposed explanation for a phenomenon different from recorded facts , which is useful when:

• the existing evidence is limited,
• no recognized historical methodology is available,
• or researchers want to examine a specific aspect of a historical event.

A reasonable hypothesis should give a straightforward answer with substantial explanatory power . If you also need a unique idea to write about, check our list of history topics .

Hypothesis vs. Theory

💡 hypothesis maker: what it is.

An automatic hypothesis generator is a tool that can save you time and energy. It uses advanced AI technology to create an appropriate assumption:

• The generator analyzes the variables you have input into the cycle.
• Then, it formulates the relations between them.
• Finally, it generates a hypothesis that you can use for your paper.

Our history hypothesis generator is a straightforward tool. You can use it whenever you need help inventing or wording your idea. It’s free and available all the time!

At a particular stage of your research, you will need to generate a hypothesis from a research question. A hypothesis is a statement that you will further test.

To do that, you need to take five steps:

• Define independent and dependent variables.
• Brainstorm ideas to explain the question.
• Choose the most convincing explanation.
• Formulate a statement based on this explanation.
• Check if the claim is testable in a scientific study.

✅ How to Make a Good Hypothesis in History

So that you don’t get confused when developing your historical hypothesis, let’s see what characteristics a successful one should obtain:

✍️ History Hypothesis Maker: Examples

So, you’ve read about the characteristics of a good hypothesis in history. Now you may be wondering what one actually looks like. In this section, we have listed some examples based on sample academic papers .

Italian industrial capacities were underutilized, while other Axis partners exploited their capabilities. Such countries as Hungary, Bulgaria, and Slovakia even granted loans to Nazi Germany. The WWII could have ended differently if German-Italian cooperation had been more efficient.

People from dominant racial groups deny racism because they are ignorant of human history. At the same time, minorities see the issue differently. They are aware of the records and experience systemic racism in the present.

Islamic Art has features distinctive from the Platonic influence on Islamic thought. Thus, there is a philosophical explanation of why it follows the principles of order and harmony.

The world ignored the Korean crisis in 1948 due to the situation in Germany and the deterioration of Soviet-American relations.

Thanks for reading!

If you’re working on a history paper, try out our automatic hypothesis generator. It will come up with great ideas and save you a lot of time. Use our tips and examples to make your paper and research better. Besides, share it with other students who may need our advice.

Updated: Apr 19th, 2024

• Hypothesis-Based Research | Michigan Tech
• A Brief Guide to Writing a History Paper | Harvard College
• Hypothesis Formulation | Boston University
• Developing a Hypothesis | Pressbooks

Automated Hypothesis Generation

Automated hypothesis generation: when machine-learning systems produce ideas, not just test them.

Testing ideas at scale. Fast.

While algorithms are mostly used as tools to number-crunch and test-drive ideas, they have yet been used to generate the ideas themselves. Let alone at scale.

Rather than thinking up one idea at a time and testing it, what if a machine could generate millions of ideas automatically? What if this same machine would then proceed to autonomously test and rank the ideas, discovering which are better supported by the data? A machine that can even identify the type of data that could refute one’s theories and challenge existing practices.

This machine lies at the heart of SparkBeyond Discovery: its Hypothesis Engine. The engine automatically generates millions of ideas, many of them novel. Asks questions we would never think to even ask.

This Hypothesis Engine integrates the world’s largest collection of algorithms, and bypasses human cognitive bias to produce millions of ideas, hypotheses and questions in minutes. These hypotheses ensure that any meaningful signals in the data are surfaced. Then, these signals are often immediately actionable, and can be used as predictive features in machine learning models.

Going beyond the bias

Human ideation is inherently limited by cognitive bottlenecks and biases, which restrict us in generating and testing ideas at scale and high throughput. We're also limited by the speed at which we can communicate. We don’t have the capacity to read and comprehend the thousands of scientific articles and patents published every day. 

What’s more, the questions we ask are biased by our experience and knowledge, or even our mood.

In data science and research workflows, there are key bottlenecks that limit what a person or team can accomplish while working on a problem within a finite amount of time. 

For example, when exploring for useful patterns in data, a data scientist only has time to conceive, engineer, and evaluate a limited number of distinct hypotheses, leaving many areas unexplored. 

One of these areas is the gaps within an organization’s own data. This internal data may only reveal part of the story, whereas augmented external data sources can provide valuable contextual information. Without it, hypotheses based only on internal data don’t take into account the influence of external factors, such as weather and local events, or macro-economic factors and market conditions. 

Instead, by mapping out the entire spectrum of dynamics that happen on earth,SparkBeyond Discovery connects the dots between every data set that exists and offers a comprehensive viewpoint.

Tap into humanity's collective intelligence

Just like search engines crawl the web for text, our machine started indexing the code, data and knowledge on the web, and amassed one of the world's largest libraries of open-source code functions. 

Using both automation and AI, the Hypothesis Engine employs these functions to generate four million hypotheses per minute—a capacity that allows the technology to work through hundreds of good and bad ideas every second.

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Research Studio

Applications.

Null and Alternative Hypothesis Generator

Take the 4 steps to use this null & alternative hypothesis generator:

• Indicate your research group;
• Add the predicate and the outcome of your study;
• Define the control group if necessary;
• Choose the predicted effect and click “Generate now!”.

Whom or what are you analyzing in your study?

What is the activity or characteristic specific to your research group? The verb should correlate with your research group.

What are you measuring in your study? What thing does the above predicate affect?

Whom or what are you comparing with your research group? This field is optional.

Add here the effect of the predicate on the dependent variable.

Whatever quantitative study you write, you'll surely need to design a null and alternative hypothesis to test with statistical analysis in your study. Don't be scared off by these seemingly complex terms; in fact, formulating these hypotheses may be really fun, especially if you're using our simple, free online tool.

⭐️ Null Hypothesis Generator: the Benefits

• ⚪ How to Use the Tool
• 🔠 Null Vs. Alternative Hypothesis
• 📊 How to Choose between Them

🔗 References

⚪ null hypothesis generator: how to use it.

Let's first clarify how our automated null hypothesis generator can serve your research goals. Its use is an easy and intuitive process that requires little onboarding. Feel free to create a hypothesis for your essay using these steps:

• Indicate the subject of your study (people, processes, or phenomena you're going to examine) – it will be your experimental group.
• Stipulate the activities you expect to measure (that will be the action of your subject).
• Point out the measure (variable) you plan to measure.
• Add a comparison group that will serve as a control for your experimental group.
• Specify the expected effect of the relationship measurement – as we're talking about a null hypothesis here, you should indicate a negative effect.

After you feed that data into the online null hypothesis generator, you will get a well-formulated sentence reflecting your assumed null relationship (that is, an absence of a statistically significant relationship). The same goes for the alternative hypothesis generator, with the only difference in the expectation of a positive effect.

🔠 How to Generate a Null and Alternative Hypothesis

Now it's time to clarify the distinctions between null and alternative hypotheses to give you clear guidance on their formulation.

In other words, these two claims should contradict each other, with one stating that one variable has a visible effect on the other and the second stating that there is no such effect at all.

So, how can you apply these definitions to practice and transform your research question into workable hypotheses?

Here is a handy table with explanations and illustrations of how this happens.

Use this principle for formulating your hypothesis from any other research question you might want to explore. Think of it in the following terms: the null hypothesis stands for no effect, and an alternative hypothesis assumes the existence of that effect.

📊 How to Choose between Null and Alternative Hypothesis

Let's first depart from question about choosing one of the hypotheses, as in most cases, they work in tandem and are inseparable.

So, the good news is that you won't need to choose one of them for your study; they will be presented as a pair of hypotheses. Depending on your study findings, one will be proved, and the other will be disproved.

Now, we have come to the point of using statistics to detect which one is good. In other words, you will need to choose which hypothesis works out and explains the relationship you're examining better than its counterpart. Here are the simple steps you should take to prove and disprove your academic assumptions.

Step 1 - Collect Relevant Data

Once the hypotheses are ready, it's time to check whether the data proves or disproves any of them. Thus, for instance, if you measure the correlation between a person's leadership style and personality type, you should evaluate every respondent's leadership style and personality type with specific quantitative questionnaires.

Step 2 - Use Statistical Analysis

The collected data should be fed into statistical software (e.g., SPSS ) for analysis. You will have a series of quantitative measures for every respondent and every variable neatly organized in rows and lines, assigning specific categories to each number.

Then you can run a t-test or a correlation test depending on the relationship you're studying and see what results you get. Let's talk about the example given above. You will need to run a correlation test for leadership style and personality type measures to see whether the Pearson correlation score is statistically significant.

Step 3 - Reject One Hypothesis & Prove the Other One

Now that you have the statistical analysis results in front of you, it's time to interpret them and reject one of the mutually exclusive hypotheses.

Continuing with the example given above, you will need to see whether your resulting Pearson correlation is high or low:

• Coefficients below 0.5 show a loose correlation;
• 0.5 to 0.7 signify a moderate correlation;
• 0.7-0.9 stands for a high correlation.

Thus, if you see a figure below 0.5, you can consider your null hypothesis proven – there is no significant correlation between leadership style and personality type in the sample of your participants. If your figure is 0.5 and higher, you can consider your alternative hypothesis validated – there is a correlation between a leadership style and a personality type in your chosen sample.

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Updated: Apr 9th, 2024

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