Solve core probability and statistics questions
Company: Netflix
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Onsite
Answer the following short theory/computation questions (as in an OA multiple-choice section). Provide the key formula and a brief explanation.
1. **Bayes’ rule**: Given a prior \(P(A)\), and likelihoods \(P(B\mid A)\), \(P(B\mid A^c)\), compute \(P(A\mid B)\).
2. **Why add controls in regression?** Explain when adding control variables helps estimate a causal effect, and when it can hurt.
3. **CLT**: State the Central Limit Theorem and what it implies about the sampling distribution of a sample mean.
4. **Uniform distribution moments**: For \(X\sim \text{Unif}(a,b)\), compute \(E[X]\) and \(\mathrm{Var}(X)\).
5. **Hypothesis testing / t-statistic**: For comparing two means (or a regression coefficient), write the form of a t-statistic and how it’s used.
6. **Effect size vs MDE**: Relate effect size, variance, sample size, significance level \(\alpha\), and power \(1-\beta\) to the minimum detectable effect (MDE).
Quick Answer: This question evaluates proficiency in core probability and statistical inference—covering Bayes' rule, causal controls in regression, the Central Limit Theorem, uniform distribution moments, t-statistics, and minimum detectable effect—testing both conceptual understanding and practical computational fluency in the Statistics & Math domain.