# AB Testing and Applied Statistics (Data Analytics)

> Hypotheses, sample size, statistical significance, p-value, Student's t-test, chi-square, interpretation

- 20 interview questions
- Mid-Level
- [Interview Questions: Data Analytics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions.md)

## 1. What is a null hypothesis (H0) in an AB test?

**Answer**

The null hypothesis (H0) states that there is no significant difference between the two variants being tested. In AB testing, H0 asserts that any observed difference between the control group (A) and treatment group (B) is due to chance rather than the effect of the change being tested. The purpose of the statistical test is to determine whether the data allows us to reject H0.

## 2. What is an alternative hypothesis (H1) in an AB test?

**Answer**

The alternative hypothesis (H1) is the opposite of the null hypothesis and asserts that there is a real difference between variants. In AB testing, H1 states that the change being tested has a measurable effect on the observed metric. If the statistical test allows us to reject H0 with sufficient confidence, we accept H1 as true.

## 3. What is the p-value in an AB test?

**Answer**

The p-value represents the probability of observing the obtained results (or more extreme) if the null hypothesis were true. The lower the p-value, the more unlikely it is that the results are due to chance. By convention, if the p-value is below the significance threshold (often 0.05), we reject H0 and consider the result statistically significant.

## 17 more questions available

- What is statistical significance in an AB test?
- What is a Type I error (false positive) in an AB test?

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