Statistics & Math Interview Questions

Showing 181–210 of 218 results

• TikTokMedium·Data Scientist
  Derive L1 vs L2 effects with correlation

  This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.

  Statistics & MathTechnical Screen
• TikTokHard·Data Scientist
  Use DiD for staggered treatment adoption

  This question evaluates proficiency in causal inference for panel data, specifically difference-in-differences with staggered adoption, heterogeneous and dynamic treatment effects, event-study design, pre-trend diagnostics, cluster-robust inference, and comparison with propensity-score matching.

  Statistics & MathTechnical Screen
• Two SigmaEasy·Data Scientist
  Answer four core statistics questions

  This multipart problem evaluates competency in probability and mathematical statistics, covering order statistics and covariance, the rationale for degrees of freedom in sample variance, z-test sample size calculation, and a one-sided concentration inequality, categorized under Statistics & Math for a Data Scientist role and combining conceptual derivations with practical formula application. Such questions are commonly asked to verify mathematical rigor in deriving expectations and covariances, understanding sampling distributions and unbiased estimation, applying hypothesis testing power formulas, and proving probabilistic inequalities that underpin statistical reasoning.

  Statistics & MathTechnical Screen
• Two SigmaHard·Data Scientist
  Explain why the t-statistic helps

  This question evaluates understanding of the t-statistic, standardized effect sizes, standard errors, hypothesis testing, and interpretation of statistical evidence within the Statistics & Math domain, and it targets conceptual understanding with practical application implications.

  Statistics & MathTechnical Screen
• Two SigmaHard·Data Scientist
  Why Use a t-Statistic?

  This problem tests your conceptual understanding of the t-statistic, specifically how sample parameters influence its value and how it bridges the gap between estimation and hypothesis testing.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingQuantitative Finance
• Two SigmaEasy·Data Scientist
  Why is the t-statistic useful

  This question tests the candidate's conceptual understanding of the t-statistic, specifically its role in hypothesis testing when population parameters are unknown.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingInference
• Two SigmaHard·Data Scientist
  Why use the t-statistic?

  This question tests the conceptual understanding of the t-statistic, specifically why it is used instead of the Z-statistic when population parameters are unknown and how it accounts for uncertainty in sample variance.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingT-Distribution
• UberMedium·Data Scientist
  Analyze Cancellation Change with Statistics

  This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Apply instrumental variables under interference

  This question evaluates understanding of causal inference with instrumental variables in the presence of interference, testing skills in defining units and aggregation levels for market‑level spillovers, articulating IV assumptions (relevance, exclusion restriction, independence, monotonicity), and formulating estimation frameworks such as two‑stage least squares. Commonly asked in Statistics & Math interviews for data scientist roles because networked marketplaces invalidate simple A/B tests, it sits in the econometrics/causal inference domain and primarily assesses practical application of IV methods while requiring conceptual understanding of identification, robustness diagnostics, and sensitivity analysis.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Compare Two Coin Proportions

  This question evaluates proficiency in statistical inference for proportions, including hypothesis formulation, comparison of two independent samples, confidence interval interpretation, and interpretation of p-values and sampling variability.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Compute A/B sample size under clustering

  This question evaluates competency in experimental design and statistical inference for A/B testing, specifically sample size calculation for two-sample proportions with unequal allocation, clustering effects (ICC and design effect), and adjustments for bot removal and data-quality attrition.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Derive a CDF from a PDF

  This question tests the ability to derive a Cumulative Distribution Function (CDF) from a given Probability Density Function (PDF), ensuring proper integration and boundary conditions.

  Statistics & MathTechnical ScreenProbabilityCalculusStatistics
• UberHard·Data Scientist
  Design an A/B test; choose Z vs T

  This question evaluates a candidate's competency in experimental design and statistical inference, covering sample-size and power calculations, the choice between Z and T tests, sequential testing/alpha-spending strategies, and causal-inference planning for non-randomized rollouts.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Formulate hypotheses and compute AB test significance

  This question evaluates a data scientist's competency in experimental design and statistical inference for A/B testing, covering hypothesis formulation, difference-in-proportions testing and confidence intervals, guardrail analysis, multiple-testing correction, interim alpha-spending approaches, and variance-reduction techniques such as CUPED.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Measure rider incentive causal ROI

  This question evaluates a data scientist's competency in causal inference and experimental design, marketplace health metric construction, ROI estimation, and the handling of selection bias, spillover effects, multiple testing, and heterogeneity in two-sided platforms.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Should Uber double member discounts?

  This problem tests your ability to design a controlled experiment for a two-sided marketplace and evaluate whether a discount-doubling strategy is statistically and economically viable.

  Statistics & MathTechnical ScreenA/B TestingCausal InferenceMarketplace Economics
• UpstartMedium·Data Scientist
  Analyze HT vs HH stopping-time probabilities

  This question evaluates understanding of discrete probability, stopping-time analysis, pattern occurrence in Bernoulli sequences, derivation of PMFs and expectations, and the ability to generalize results to biased coins.

  Statistics & MathTechnical Screen
• UpstartEasy·Data Scientist
  Calculate Particle Survival Probability After Time t

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

  Statistics & MathTechnical Screen
• UpstartEasy·Data Scientist
  Correct length-biased sampling from family-size survey

  This question evaluates understanding of length-biased sampling and inference under sampling bias, specifically how sampling by individuals (children) differs from sampling by households when estimating family-size probabilities.

  Statistics & MathTechnical Screen
• UpstartMedium·Data Scientist
  Solve drunk-passenger probability and simulate outcome

  This question evaluates probabilistic reasoning and stochastic-process intuition, including use of invariant arguments, closed-form derivations, statistical estimation via Monte Carlo simulation, and analysis of algorithmic time and space complexity.

  Statistics & MathTechnical Screen
• Virtu FinancialMedium·Data Scientist
  Compute probability in urn with green returns

  Compute probability in urn with green returns 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
• Virtu FinancialMedium·Data Scientist
  Compute probability with colored balls draws

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute probability with colored balls draws states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• VoleonMedium·Quantitative Researcher
  Bound R-Squared for Individual and Combined OLS Regressions

  Derive in-sample OLS R-squared bounds and the joint-model range from individual scores, including intercept assumptions, predictor correlation, and out-of-sample exceptions.

  Statistics & MathTechnical Screen
• VoleonHard·Data Scientist
  Compute robust inference under skew and outliers

  This problem tests your ability to design a robust statistical framework for comparing two groups with highly skewed, heavy-tailed data and potential outliers, moving beyond simple t-tests.

  Statistics & MathTechnical ScreenStatisticsA/B TestingRobust Inference
• VoleonMedium·Data Scientist
  Diagnose and interpret regression assumptions

  This problem tests your ability to diagnose violations of OLS assumptions in count data and determine the appropriate corrective modeling strategy.

  Statistics & MathTechnical ScreenRegressionGLMDiagnostics
• VoleonMedium·Data Scientist
  Explain P-Value Reporting and Bootstrap for Coefficient Estimation

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain P-Value Reporting and Bootstrap for Coefficient Estimation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• VoleonEasy·Data Scientist
  Solve Markov, OLS-rotation, and coin-toss probability

  This multi-part question evaluates understanding of Markov chain and stochastic matrix properties, linear-algebra facts about eigenvalues and stationary distributions, invariance and sampling-distribution properties of OLS under orthonormal rotations, and discrete stopping-time probability reasoning for coin-flip processes.

  Statistics & MathTechnical Screen
• WaymoMedium·Data Scientist
  Compare Two Rare-Event Rates with Different Exposure

  Compare sparse event rates across unequal mileage using a two-sample Poisson framework, exact conditional testing, and honest uncertainty.

  Statistics & MathTechnical Screen
• WaymoEasy·Data Scientist
  Compute probability match lasts 5 games

  This question evaluates understanding of discrete probability, sequential best-of-n match modeling, Monte Carlo simulation for probability estimation, and interval estimation for proportions.

  Statistics & MathTechnical Screen
• WaymoMedium·Data Scientist
  Estimate Event Rates from Unequal-Probability Samples

  Establish event rates from unequal sampling with inverse-probability weighting, exposure-aware denominators, and efficient stratified allocation.

  Statistics & MathTechnical Screen