What I understand about hypothesis testing

Definition

hypothesis testing is a template for making decisions using data and statistics.

Conception

There are two contradictory hypotheses about a scenario from which the data was collected. They are called:

Null hypothesis: The default assumption. Usually means there is no effect or no difference.
Alternative hypothesis: the opposite assumption. usually means there is an effect or a different one.
Reject or Accept
Use dataset to decide:
Reject the null hypothesis: This means that the data show it is possible that the null hypothesis is false. The data set does not support the null hypothesis.
Accept null hypothesis (not reject null hypothesis): that mean the dataset show it ti possible that null hypothesis is true. The data set supports the null hypothesis.

When you reject the null hypothesis, but the reality is that the null hypothesis is true.
You think something is effect or different, but it is not true.
Type II Error: The "False Negative"
When you accept (do not reject) the null hypothesis, but the reality is the null hypothesis is false.
You think something has no effect or is not different, but it is true

We calculate P-value by statistical method.

What is P-value. P-value mean assume H0 is true, the probability of current data set can be selected. Then we decide an α (alpha) value. usually 5%.
What is α(alpha).α(alpha) means the threshold that we can accept for the type I error.
Low α (alpha) means the chance of Type I error (the "false positive") became low, but the Type II error (the "false negative") became high.
Use low α (alpha) for life-related things, such as medical and court cases. If P-value < α (alpha): reject null hypothesis, accept alternative hypothesis If P-value > α (alpha): accept null hypothesis, reject alternative hypothesis