# Statistics For data Science Interview Questions

#### Questions on Basic Terminology

1. Why study Statistics?
2. What is the difference between Population and Sample?
3. What do you understand by census and Survey?
4. How to describe Parameter and Statistics?
5. How to define Descriptive vs Inferential Statistics? Explain with an example.
6. Types of variables in Statistics?
7. Difference between Nominal vs Ordinal variables and discrete vs Continuous variables.
8. What are the measures of Central Tendencies? Explain with suitable examples.
9. Describe with specific examples on when to use mean, when to use median and when to use mode?
10. Explain the measures of variability and spread with example?
11. What is the mathematical significance variance and Standard Deviation and how to calculate them?
12. How to define Outliers in your data set and what are the techniques to deal with outliers?
13. Explain the Box Plot.
14. Describe z-score also known as a standard score.

#### Questions on Probability Basics

1. Probability vs Statistics
2. Explain about Joint Probability, Union Probability and Marginal Probability.
3. Explain about Mutually exclusive vs Independent Events.
4. Conditional Probability use cases in Data Science.
5. What is the Monty Hall Problem?
6. Explain the use and importance of Bayes’ Theorem in Data Science study.

#### Performance Metrics

1. Explain what is confusion matrix and various terms associated with it.
2. What is Recall and how it is different from Precision? Give examples of each.
3. What is the True Positive Rate?
4. How to differentiate between F1 Score and Accuracy?

#### Probability Distributions

1. Describe Probability Distribution Function (PDF) and Probability Mass Function (PMF).
2. Describe skewed vs normally distributed Data.
3. What is Kurtosis?
4. What is Bernoulli distribution and How is different from Geometric Distribution?
5. What is Binomial Distribution?
6. What is Exponential Distribution and how it is different from Poisson distribution?
7. What are the properties of Normal or Gaussian distribution?

1. Explain the usage of Central Limit Theorem in Data Science.
2. What are the three important properties of Central Limit Theorem?
3. What do you mean by Sampling Distribution?
4. What is the Standard error of the Mean?
5. Explain Confidence Level and Confidence Interval.
6. What is the difference between Standard Error and Margin of error? Are the Same?
7. Explain Hypothesis Testing.
8. What is significance Level in Hypothesis Testing?
9. How to determine the Critical Region?
10. What are p-value and q-value?
11. How to validate the Null Hypothesis?
12. What do you understand by one-tailed and two-tailed test in hypothesis testing?
13. Explain about Type I and Type II errors.
14. What are the tests done to check the sampling distribution of means?
15. Explain the z test and t-test and their properties.
16. What are the tests done to check the Sampling Distribution of Variance?
17. Explain X-Square (Chi-Squared) and F test and their properties.
18. What do you understand by “Goodness of Fit”?
19. Explain the use case of ANOVA.