# Null & 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”.

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: 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.

## 🔠 Null Vs. Alternative Hypothesis: Definition & Examples

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 & 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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