This video explains both the null and alternate hypotheses: What is a Null Hypothesis? Watch this video

The **null hypothesis**, H0 is the commonly accepted fact; it is the opposite of the alternate hypothesis. Researchers work to reject, nullify or disprove the null hypothesis. Researchers come up with an alternate hypothesis, one that they think explains a phenomenon, and then work to reject the null hypothesis.

**if the expected earnings for the gambling game are truly equal to zero, then any difference between the average earnings in the data and zero is due to chance**.

Question | Null Hypothesis |
---|---|

Do cats care about the color of their food? | Cats express no food preference based on color. |

## Hypothesis testing. Null vs alternative

## Null hypothesis vs. alternative hypothesis

Where the null hypothesis states that there is no actual difference among a set of figures, conversely, the alternative hypothesis suggests there is a distinction between the figures. Therefore, the null hypothesis contradicts the alternative hypothesis.

Statisticians and analysts develop the alternative hypothesis to describe a set of circumstances or explain the differences in statistical relationships. Using the alternative hypothesis as a guide, researchers perform experiments and conduct research to disprove and reject the null hypothesis.

## What is a null hypothesis?

A null hypothesis is a type of hypothesis that proposes there is no difference or meaningful relationship between two things, either tangible or abstract. It is unnecessary to believe the null hypothesis is true to test it, and the word “null” highlights that scientists are actually attempting to invalidate the stated null hypothesis. The null hypothesis is important because it acknowledges whether the established data and findings occurred because of chance alone.

## How do null hypotheses work?

A null hypothesis proposes there are no differences between a set of relationships or variables, and its alternative hypothesis proposes that differences among those relationships exist. Therefore, researchers presume that the null hypothesis is accurate until there is sufficient and statistically significant data that proves otherwise.

Researchers use these guidelines for hypothesis testing:

When testing a hypothesis, researchers use p-value as evidence against a null hypothesis. In this way, smaller p-values show strong statistical data and research that disproves the null hypothesis. Researchers perform significance tests in order to show confidence in the null hypothesis. Significance testing is also used to investigate whether the data is due to chance.

During testing, statisticians face two scenarios:

Rejection of the null hypothesis doesnt mean the experiment didnt find the required answers. Instead, it indicates a need for further experimentation to see if there is a relationship between the variables in the claim.

## Tips for stating null hypotheses

Here are some tips for stating null hypotheses:

**Think of the null hypothesis as fact**

Think of the null hypothesis as a fact and the alternative hypothesis as an opinion or belief. To state the null hypothesis, you must regard it as the status quo or the way things currently exist. Therefore, if you accept the null hypothesis as fact, then the alternative hypothesis is the statement that disputes that fact. Researchers and statisticians aim to disprove the null hypothesis while proving the alternative hypothesis to be true.

**Create the null hypothesis**

In order to create the null hypothesis, researchers examine the problem they are trying to solve and determine the questions they are trying to ask. Typically, the null hypothesis is a direct representation of the expected outcome. They start by asking a question, then rephrasing that question as a statement that assumes no relationship between the two variables.

**Identify possible circumstances**

When stating the null hypothesis, statisticians must identify all outcomes. For example, after examining a problem and identifying questions to ask, statisticians conclude that the null hypothesis is the expected outcome. Next, they develop an alternative hypothesis that works to reject the expected outcome.

In this way, researchers try to predict all circumstances and either reject the null hypothesis and accept the alternative hypothesis or fail to reject the null hypothesis.

## Null hypothesis example

Here is an example of how you can use a null hypothesis:

**State the null hypothesis**

A school districts superintendent claims their districts high school math students receive average scores of eight out of 10 on their math tests. Here, the null and alternative hypotheses would be:

**Test the null hypothesis**

To test the validity of the null hypothesis:

**Calculate and interpret the results**

When calculating the average test scores, it is important to note that hypothesis testing assumes the null hypothesis is true, unless proven otherwise. Therefore, the claim that the districts high school math students receive average scores of eight out of 10 on tests provides data that suggests if eight out of 10 is the average, then the scale of outcomes can be any value from 7.2 to 8.8, as the population mean is 8.0. If the calculated average is any value outside this range, you can reject the null hypothesis because the average scores would not be eight out of 10.

## FAQ

**What is an example of a null hypothesis and alternative 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).

**What is null hypothesis in simple terms?**

**a hypothesis that says there is no statistical significance between the two variables**. It is usually the hypothesis a researcher or experimenter will try to disprove or discredit. An alternative hypothesis is one that states there is a statistically significant relationship between two variables.