Hypothesis Testing :
Null Hypothesis (H⁰) : It states that there is no difference of certain characteristics between the sample and population data due to random chances. Eg: Assuming the researcher’s predictions are true.

Alternate Hypothesis (H1): Alternate of Null Hypothesis, It states that there is difference of certain characteristics between the sample and population data due to random chances. Eg: Assuming the researcher’s predictions are not true.

P — Value: It is the probability of outcomes more extremes than the observed outcome assuming the null hypothesis to be true. Here extreme means significance level.

Significance level: It is a criterion used for rejecting null hypothesis. Usually the alpha is set to be 5% or 1%, that is probability factor to be 0.05 or 0.01. The significance level varies depending on the business problem statement.

Confidence level: It is 1-significance level, used to show how confident you are about your conclusion.

p-value < significance value, we reject the null hypothesis
p-value > significance value, we fail to reject the null hypothesis

Refer to below jupyter notebook for code example for easy understanding.