Null Hypothesis
2026-02-28 09:11 Diff
https://brightchamps.com/en-us/math/data/null-hypothesis

There are two main methods for statistically assessing the null hypothesis. They are: Hypothesis testing and significance testing. The null hypothesis is theoretical and based on limited data. Therefore, it must be tested further to determine its accuracy.

There are two common approaches for testing a null hypothesis: using significance testing (p-values) and hypothesis testing frameworks.

Significance Testing

It is a test that aims to reject the null hypothesis and to accept the alternative hypothesis. The purpose of the test is to determine how strongly the evidence contradicts the hypothesis test results. 

Step 1: If our assumption is the null hypothesis, we should validate its prediction using significance testing.

Step 2: First, calculate the test statistics and find the p-value.

Step 3: Compare the p-value and the significance level to decide if you should accept or reject the null hypothesis. 

Step 4: The null hypothesis can be rejected if the p-value you got is less than the significance level. However, if the p-value we have is greater than the significance level, then we simply cannot reject the null hypothesis.

Hypothesis Testing

In this method, we use the data that we gathered from a sample to draw conclusions about a larger and similar population. 

Step 1: Identifying the hypothesis as null hypothesis.

Step 2: Observing and using statistical data to decide whether to reject or fail to reject the null hypothesis based on evidence.

Step 3: Here, we should watch out for two common mistakes. Sometimes we reject the null hypothesis when the result is true. Or accept the null hypothesis, when the result is false.