The choice you make when creating a hypothesis can really change the results of your statistical tests.

Here's a simple breakdown:

1. Null Hypothesis ($H_0$): This means there is no effect or no difference.

2. Alternative Hypothesis ($H_a$): This shows that there is an effect or a difference.

To make decisions about these hypotheses, we use something called significance levels ($\alpha$). A common level we use is 0.05. This level helps us understand what p-values mean.

• If a p-value is less than $\alpha$, we can reject the null hypothesis ($H_0$). This means we have some evidence supporting the alternative hypothesis ($H_a$).

• If a p-value is greater than or equal to $\alpha$, we stick with the null hypothesis ($H_0$). This means we don’t have enough evidence to show there is an effect.

It's important to choose the right hypothesis. If we choose wrongly, we might make what's called type I or type II errors.