Answer core probability and inference questions
Company: Amazon
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Technical Screen
You are interviewing for a **Data Scientist** role. Explain/derive the following statistics fundamentals.
1. State the **Central Limit Theorem (CLT)**. What conditions are needed, and what does it imply about sample means?
2. What is a **p-value**? What is a common misinterpretation?
3. Define **Type I** and **Type II** errors and relate them to **significance level (α)** and **power (1−β)**.
4. How do you think about **sample size** requirements for hypothesis tests (what inputs matter)?
5. Explain the difference between **correlation** and **causation**. Give at least two reasons correlation may not imply causation in observational data.
Quick Answer: This question evaluates understanding of statistical inference and probability fundamentals, covering the Central Limit Theorem, p-values, Type I and Type II errors with power and significance, sample size considerations, and the distinction between correlation and causation.
