Understanding Hypothesis Testing

Hypothesis testing is an important part of statistics. It helps us figure out if a claim about a group of people or things is true or not.

There are two main parts to hypothesis testing:

1. Null Hypothesis ($H_0$): This is the starting point. It basically says there's no change or effect.

2. Alternative Hypothesis ($H_a$): This one says there is a change or difference.

Now, let's talk about the p-value. This number helps us see how strong the evidence is against the null hypothesis ($H_0$):

• If the p-value is low (usually less than 0.05), it means we should think about rejecting the null hypothesis. This suggests there's enough evidence to support the alternative hypothesis.

• If the p-value is high, it means we don’t have enough evidence to reject the null hypothesis. So, we stick with it for now.

In short, hypothesis testing helps us make decisions based on numbers and data.