Here is the list of the Top Statistics Interview Questions and Answers for Data Science Jobs:

Are you preparing for data science interviews?

Statistics is a critical part of the process, and here are some of the most asked interview questions along with simple answers to help you ace your next interview!

1. What is the difference between population and sample?

Population: The entire group you’re interested in studying.

Sample: A smaller subset of the population used for analysis.

2. What is the p-value, and why is it important?

P-value is the probability that the observed data could occur by chance under the null hypothesis. A low p-value (typically < 0.05) means you can reject the null hypothesis.

3. What is the Central Limit Theorem (CLT)?

CLT states that, regardless of the population distribution, the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

4. What is the difference between correlation and causation?

Correlation: A relationship or association between two variables.

Causation: One variable directly affects or causes a change in another.

5. What are Type I and Type II errors?

Type I Error: Rejecting the null hypothesis when it’s actually true (false positive).

Type II Error: Failing to reject the null hypothesis when it’s false (false negative).

6. What is multicollinearity, and how do you detect it?

Multicollinearity: Occurs when independent variables in a regression model are highly correlated. You can detect it using the Variance Inflation Factor (VIF) or by checking correlation matrices.

7. What is A/B testing, and how is it applied?

A/B testing is a hypothesis testing method used to compare two versions (A and B) to determine which one performs better. It’s widely used in marketing and UX/UI design.

8. What is heteroscedasticity?

Heteroscedasticity occurs when the variance of the residuals in a regression model is not constant across all levels of an independent variable. It can be detected through residual plots.

9. What is the difference between parametric and non-parametric tests?

Parametric tests assume the data follows a specific distribution (e.g., t-test, ANOVA).

Non-parametric tests don’t assume any particular distribution (e.g., Mann-Whitney U test, Kruskal-Wallis test).

10. Explain bias and variance in the context of machine learning models.

Bias: Error introduced by oversimplifying the model (high bias leads to underfitting).

Variance: Error from the model being too sensitive to small fluctuations in the training data (high variance leads to overfitting).