The p-value in A/B testing is a statistical measure that helps assess the evidence against a null hypothesis. A/B testing is a common experimental method used in fields such as marketing, product development, and healthcare to compare two versions (A and B) of a variable (e.g., a webpage, an advertisement, or a treatment) and determine which performs better. The p-value is a crucial component in interpreting the results of A/B tests.

# I. Key Points about P-Value in A/B Testing:

## 1. Definition:

- The p-value, or probability value, is a statistical measure that quantifies the evidence against a null hypothesis.
- In the context of A/B testing, the null hypothesis typically states that there is no real difference between the control group and the treatment group.
- The p-value represents the probability of obtaining the observed data, or more extreme results, if the null hypothesis is true.

## 2. Null Hypothesis (H0):

- In A/B testing, the null hypothesis typically states that there is no statistically significant difference between the two groups (A and B), implying that any observed difference is due to random chance.