Statistics & Math Interview Questions

Showing 1–30 of 218 results

• AirbnbMedium·Data Scientist
  Test conversion difference and adjust for clustering

  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.

  Statistics & MathTechnical Screen
• AmazonEasy·Data Scientist
  Answer core probability and inference questions

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

  Statistics & MathTechnical ScreenStatisticsA/B TestingHypothesis Testing
• AmazonMedium·Data Scientist
  Compute an A/B test p-value by hand

  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.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Compute and interpret quantile loss vs RMSE

  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.

  Statistics & MathTechnical ScreenStatisticsForecastingLoss Functions
• AmazonMedium·Data Scientist
  Compute p-values, CIs, and adjust multiples

  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.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Determine Probability of Both Children Being Boys

  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.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain P-value, Confidence Interval, and Multiple Testing Adjustments

  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.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Propensity Score Matching and Assess Covariate Balance

  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.

  Statistics & MathTechnical Screen
• AmazonMedium·Data Scientist
  Explain Statistical Outputs to Non-Technical Stakeholders

  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.

  Statistics & MathTechnical Screen
• AmazonHard·Data Scientist
  Plan and analyze an A/B test

  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.

  Statistics & MathTechnical ScreenExperimental DesignPower AnalysisClustered Data
• AppleMedium·Data Scientist
  Compare Normal and Poisson Distributions in Statistics

  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.

  Statistics & MathTechnical Screen
• AppleMedium·Data Scientist
  Compare Normal vs Poisson; test dispersion and approximate tails

  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.

  Statistics & MathTechnical Screen
• AppleEasy·Data Scientist
  How would you critique this regression?

  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.

  Statistics & MathTechnical Screen
• BBlackEdgeEasy·Data Scientist
  Solve quantitative word and algebra problems

  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.

  Statistics & MathTechnical Screen
• BByteDanceMedium·Data Scientist
  Calculate Triangle Probability From Broken Stick

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

  Statistics & MathTechnical Screen
• BByteDanceMedium·Data Scientist
  Explain Type I/II errors vs precision/recall

  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.

  Statistics & MathTechnical Screen
• BByteDanceHard·Data Scientist
  Interpret and validate regression with interactions

  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.

  Statistics & MathTechnical Screen
• C3 AIMedium·Data Scientist
  Explain the Gaussian Distribution and Its Limits

  Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.

  Statistics & MathTechnical ScreenStatisticsProbabilityAnomaly Detection
• CVS HealthMedium·Data Scientist
  Explain p-value and choose correct test

  This question evaluates a candidate's understanding of statistical inference, hypothesis testing, and robust test selection, focusing on p-value interpretation and choosing between paired vs independent and parametric vs rank-based tests.

  Statistics & MathTechnical Screen
• Capital OneMedium·Data Scientist
  Calculate break-even new customers for a 30% Rent-a-Home discount

  A Capital One data-scientist technical screen on break-even and unit economics for a 30%-off Rent-a-Home promotion. You set up and solve the break-even equation for incremental new customers, size it against the 2M cardholder base, then re-solve under spend-uplift assumptions and explain why the required count nearly doubles.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Calculate first-year profit margin

  This question evaluates a data scientist's quantitative reasoning about unit economics and financial metrics, including understanding fixed versus variable costs and computing profit margin as a percentage.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Calculate profit and break-even across pricing models

  This question evaluates understanding of unit economics, break-even analysis, and pricing-model effects on profitability by requiring computation of revenue, variable and fixed costs, and profit under per-GB and two-tier pricing scenarios.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Compare average profit across mix scenarios

  This question evaluates understanding of expected value, weighted averages, and profit-margin analysis within the Statistics & Math domain for a Data Scientist role. It is commonly asked to probe quantitative reasoning about pricing and cost trade-offs, and it tests practical application of arithmetic and scenario-based profit modeling rather than abstract theoretical concepts.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Compare average unit profit under mix shift

  This question tests the candidate's ability to calculate weighted averages and understand how changes in sales volume distribution (mix shift) affect the overall average profit per unit.

  Statistics & MathTechnical ScreenStatisticsWeighted AverageUnit Economics
• Capital OneEasy·Data Scientist
  Compute Groupon unit economics and break-even

  A Capital One data science technical screen case on restaurant unit economics with a Groupon-style voucher. You compute baseline daily profit from a 20-table day, derive the break-even check for a voucher table under a $30-for-$15 offer with a 40% commission, profit a mixed 25-table day, and decompose the profit change into traffic, check size and voucher cost. Full worked arithmetic, a sensitivity solve, and the incrementality follow-ups an interviewer expects.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Compute current annual profit

  This question evaluates numerical proficiency in financial arithmetic, unit-aware quantitative reasoning, and per-customer business-metric computation within a loyalty program context.

  Statistics & MathTechnical Screen
• Capital OneEasy·Data Scientist
  Compute incremental profit, breakeven, and revenue sensitivity

  A Capital One data scientist technical-screen case on cost accounting and profitability modeling. Candidates compute the Year-1 incremental profit of a new vegan burger line, decompose it into fixed versus variable components, derive breakeven volume, and run a ±10% sensitivity analysis.

  Statistics & MathTechnical Screen
• Capital OneMedium·Data Scientist
  Compute optimal stopping in a die-rolling game

  This question evaluates a candidate's grasp of optimal stopping theory, expected value computation, and finite-horizon decision-making under uncertainty, including recognition of threshold-based policies and backward-induction reasoning relevant to data scientist roles.

  Statistics & MathTechnical Screen
• Capital OneMedium·Data Scientist
  Compute profit and break-even for memberships

  This problem tests your ability to model unit economics for a subscription-based loyalty program and perform sensitivity analysis to determine the break-even point for a business intervention.

  Statistics & MathTechnical ScreenEconomicsLinear ModelingBreak-even Analysis
• Capital OneMedium·Data Scientist
  Compute required output to recover 10% investment

  This question evaluates a candidate's ability to perform cost-volume-profit analysis and quantitative financial modeling, assessing skills in algebraic formulation of profit equations, capacity constraint evaluation, and derivation of generalized feasibility formulas.

  Statistics & MathTechnical Screen