Statistics & Math interview questions

Showing 1–30 of 439 results

• Understand P-Value's Role in Product Decision-MakingEasy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand P-Value's Role in Product Decision-Making states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AdobeData ScientistStatistics & MathOnsite
• Test conversion difference and adjust for clusteringMedium

  This question evaluates proficiency in statistical inference for A/B testing—estimating and comparing conversion proportions, conducting two-sided hypothesis tests, adjusting for day-level clustering using ICC and design-effect corrections, and performing power and sample-size calculations; it belongs to the Statistics & Math domain for a Data Scientist role and combines conceptual understanding with practical application. It is commonly asked to assess an interviewee's ability to interpret conversion uplift under realistic experimental constraints, account for intra-cluster correlation when estimating effective sample sizes and uncertainty, and reason about experiment duration and robustness checks.

  AirbnbData ScientistStatistics & MathTechnical Screen
• Answer core probability and inference questionsEasy

  Tests fundamental statistical inference and probability concepts essential for experimentation and data analysis, including the Central Limit Theorem, hypothesis testing errors, and causal reasoning.

  AmazonData ScientistStatistics & MathTechnical Screen
• Calculate A/B sample size, CI, decision rulesMedium

  This question evaluates skills in experimental design and statistical inference, including sample size calculation, hypothesis testing, confidence interval interpretation, multiple comparison control, variance reduction techniques, and operational considerations for A/B testing.

  AmazonData ScientistStatistics & MathOnsite
• Compute an A/B test p-value by handMedium

  This question evaluates understanding of hypothesis testing for proportions, specifically the two-proportion z-test, p-value computation and interpretation of statistical significance in an A/B test with binary outcomes.

  AmazonData ScientistStatistics & MathTechnical Screen
• Compute and Explain Mean and VarianceMedium

  This question tests your understanding of statistical estimators for mean and variance, the derivation of Bessel's correction, catastrophic cancellation in naive computations, and numerically stable single-pass algorithms like Welford's.

  AmazonApplied ScientistStatistics & MathOnsite
• Compute and interpret quantile loss vs RMSEMedium

  This question tests the ability to distinguish between point-prediction error metrics and quantile loss, and the practical implications of choosing an evaluation metric in the context of asymmetric business costs.

  AmazonData ScientistStatistics & MathTechnical Screen
• Compute p-values, CIs, and adjust multiplesMedium

  This question evaluates a candidate's proficiency in statistical inference, covering hypothesis testing and test selection, confidence interval construction, multiple-testing corrections, error types, and asymptotic reasoning.

  AmazonData ScientistStatistics & MathTechnical Screen
• Determine Probability of Both Children Being BoysMedium

  Evaluates conditional probability reasoning in the classic two-children problem with different observation mechanisms. Strong answers enumerate ordered outcomes, condition correctly, and compare at-least-one, older-child, and random-child cases.

  AmazonData ScientistStatistics & MathTechnical Screen
• Estimate Treatment Effects Using PSM, DiD, and DML MethodsHard

  Estimate Treatment Effects Using PSM, DiD, and DML Methods evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistStatistics & MathOnsite
• Explain P-value, Confidence Interval, and Multiple Testing AdjustmentsMedium

  Evaluates A/B testing inference fundamentals, including p-values, confidence intervals, multiple testing adjustments, Type I and Type II errors, z-tests versus t-tests, and CLT versus LLN. Strong answers connect definitions to experiment decisions and common pitfalls.

  AmazonData ScientistStatistics & MathTechnical Screen
• Explain Propensity Score Matching and Assess Covariate BalanceMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Propensity Score Matching and Assess Covariate Balance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistStatistics & MathTechnical Screen
• Explain Statistical Outputs to Non-Technical StakeholdersMedium

  Explain Statistical Outputs to Non-Technical Stakeholders evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistStatistics & MathTechnical Screen
• Identify P-Value Limitations and Complementary ApproachesMedium

  Identify P-Value Limitations and Complementary Approaches evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistStatistics & MathOnsite
• Mean and Variance Through Maximum LikelihoodMedium

  Derive the maximum likelihood estimators (MLE) for the mean and variance of a univariate Gaussian distribution, and analyze the bias of the resulting variance estimator.

  AmazonApplied ScientistStatistics & MathOnsite
• Plan and analyze an A/B testHard

  This question tests the ability to design a statistically rigorous A/B test by accounting for clustered randomization, sequential testing boundaries, and multiple comparison corrections.

  AmazonData ScientistStatistics & MathTechnical Screen
• Validate DID and IV assumptions rigorouslyHard

  This question evaluates mastery of causal inference and applied econometrics, specifically difference-in-differences, two-way fixed effects, staggered adoption and treatment heterogeneity, instrumental variables (2SLS), clustering, and GMM-based inference.

  AmazonData ScientistStatistics & MathHR Screen
• Compare Normal and Poisson Distributions in StatisticsMedium

  Evaluates statistical understanding of Normal and Poisson distributions for counts and continuous measurements. Strong answers compare support, parameters, use cases, Normal approximation conditions, and mean-variance derivations.

  AppleData ScientistStatistics & MathTechnical Screen
• Compare Normal vs Poisson; test dispersion and approximate tailsMedium

  This question evaluates a data scientist's competency in statistical inference for count data, covering MLE and asymptotic variance estimation, Wald confidence intervals, tests for equidispersion versus overdispersion, Normal approximation with continuity correction for Poisson tails, and model selection diagnostics.

  AppleData ScientistStatistics & MathTechnical Screen
• Differentiate P-value and Confidence Interval in StatisticsMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Differentiate P-value and Confidence Interval in Statistics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AppleData ScientistStatistics & MathOnsite
• How would you critique this regression?Easy

  An Apple Data Scientist technical-screen statistics question that asks you to critique another analyst's linear-regression workflow for modeling website dwell time. It tests OLS assumptions (and why marginal normality of Y is not one of them), model-selection and overfitting bias from exhaustive interaction search, diagnostics and validation, and the distinction between prediction, inference, and causal goals on skewed outcome data.

  AppleData ScientistStatistics & MathTechnical Screen
• Determine Worker Counts from Production RatiosEasy

  This problem tests mathematical modeling and systems of linear equations by requiring the candidate to derive specific headcount requirements based on production demand and individual productivity constraints.

  BarclaysData ScientistStatistics & MathOnline Assessment
• Solve quantitative word and algebra problemsEasy

  This question evaluates quantitative reasoning and arithmetic/algebraic manipulation, covering unit conversion, percentage change, systems of linear equations, time-rate calculations, and series/summation properties.

  BlackEdgeData ScientistStatistics & MathTechnical Screen
• Evaluate a verbal reasoning passageEasy

  This question evaluates verbal reasoning and logical inference by requiring classification of statements as True, False, or Cannot Say from a short passage; it belongs to the Cognitive Ability/Logic domain and targets conceptual understanding rather than practical application.

  BlackRockSoftware EngineerStatistics & MathOnline Assessment
• Calculate Probability and Statistics for Dice Roll OutcomesEasy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability and Statistics for Dice Roll Outcomes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Boston Consulting GroupData ScientistStatistics & MathOnline Assessment
• Calculate Probability of Heads and Red Balls DrawingEasy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads and Red Balls Drawing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Boston Consulting GroupData ScientistStatistics & MathOnline Assessment
• Compute posterior and predictive coin probabilitiesMedium

  This question evaluates Bayesian inference and probabilistic reasoning, focusing on posterior updating, posterior predictive probability, and the conditional expectation of a stopping time given observed outcomes.

  Boston Consulting GroupData ScientistStatistics & MathOnline Assessment
• Calculate Triangle Probability From Broken StickMedium

  This question evaluates a candidate's understanding of geometric probability, continuous random variables, and spatial reasoning within probabilistic models.

  ByteDanceData ScientistStatistics & MathTechnical Screen
• Explain Type I/II errors vs precision/recallMedium

  This question evaluates understanding of Type I and Type II errors in hypothesis testing and binary classification, their correspondence to false positives and false negatives, and how those concepts relate to precision and recall within the Statistics & Math domain.

  ByteDanceData ScientistStatistics & MathTechnical Screen
• Interpret and validate regression with interactionsHard

  This question evaluates understanding of regression modeling with interaction terms, binary outcome models and odds/marginal-effect interpretation, causal inference concepts like endogeneity and instrumental variables, model diagnostics and standard-error choices, and count-data modeling.

  ByteDanceData ScientistStatistics & MathTechnical Screen