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Practice the statistics and probability concepts tested in data science and analytics interviews: distributions, hypothesis testing, p-values, confidence intervals, Bayes' theorem, and sampling.
Answer a real Statistics interview question before you browse the list. No signup needed.
Which flag is NOT included when strict: true is set in tsconfig?
Practice in a session
10 questions from Statistics, one after another, then your score and streak.
Core topics are probability distributions (normal, binomial, Poisson), hypothesis testing and p-values, confidence intervals, the Central Limit Theorem, Type I/II errors and statistical power, correlation vs causation, Bayes' theorem, and sampling/bias. Most data science and analyst loops include a dedicated statistics screen.
For most product data scientist and analyst roles, conceptual fluency matters more than derivations: you should be able to explain what a p-value means, when to use a t-test vs a chi-square test, and how confidence intervals behave - not prove the CLT. Research and ML scientist roles go deeper into probability theory and estimation.
Misinterpreting the p-value (it is not the probability the null is true), confusing statistical significance with practical/effect size, ignoring multiple-comparisons inflation, and conflating correlation with causation. Interviewers probe these exact misconceptions.
A working grasp of Bayes' theorem - priors, likelihoods, posteriors, and base-rate reasoning - is commonly tested, including classic conditional-probability puzzles (e.g. disease testing with a low base rate). Full Bayesian modeling is usually only required for specialized roles.
Probability questions cover combinatorics, conditional probability, expected value, independence, and common distributions, often as quick mental-math or brain-teaser style problems. Statistics questions focus on inference from data - estimating parameters and testing hypotheses.