Introduction to Statistics and Experimentation Questions
Statistics and experimentation skills are crucial for data scientists, so there are typically multiple statistics rounds in the interview process. You might receive a statistics technical screening, followed by 1-2 rounds in the final on-site interview(s). Given the prevalence of A/B testing, one of these rounds may focus specifically on A/B testing.
What to expect
Statistics interview questions are presented in the following ways:
- Numerical: These questions require you to use a statistical method, formula, or equation to solve a math problem. You’re tested on your ability to understand and apply the correct statistical formula or method.
Example: A $5 discount coupon is given to N riders. The probability of using a coupon is P. What is the expected cost for the company?
- Conceptual: These questions focus on your conceptual understanding of statistical theories, definitions, and methods.
Example: What are the assumptions of linear regression?
- Applied: These questions test both your conceptual and applied statistical knowledge. You’re given a business problem or scenario and then asked a series of questions related to multiple statistics concepts.
Example: How much incremental revenue can a food delivery company make if it expands to a new vertical like grocery delivery?
Given the importance of statistics skills for data science roles, this course covers a robust range of potential statistical topics that may appear in your interviews. These topics fall into the following categories:
- Data preprocessing
- Probability and regression
- Hypothesis tests and confidence intervals
- Power analysis and impact sizing
- Experimentation
In each of these modules, you’ll find lessons that provide an overview of core statistics concepts, interactive practice questions, and mock interviews. Use these resources to refresh your statistics knowledge and practice applying it to interview questions.