Statistics & Math interview questions

Showing 151–180 of 439 results

• Generate Uniform Samples and Estimate PercentilesMedium

  This problem tests the ability to generate uniform spatial samples using standard random functions and the mathematical application of linear interpolation for percentile estimation from binned data.

  GoogleData ScientistStatistics & MathTechnical Screen
• Generate values by weighted probabilitiesMedium

  This question evaluates understanding of probabilistic sampling, numerical stability, input validation, and algorithmic trade-offs for streaming weighted random draws, including generator determinism via seeding and methods such as CDF/binary-search versus alias for performance.

  GoogleMachine Learning EngineerStatistics & MathOnsite
• Infer distribution and choose robust statisticsMedium

  This problem tests the ability to diagnose non-normal data distributions (specifically zero-inflated, heavy-tailed data) and select appropriate statistical metrics for hypothesis testing.

  GoogleData ScientistStatistics & MathTechnical Screen
• Interpreting a Dosage-Recovery Regression: P-values, CIs, CausalityMedium

  Given a fitted dosage-vs-recovery-time regression, explain exactly what the reported p-value and confidence interval mean, what assumptions back that inference, how a confidence interval differs from a prediction interval, and why the result alone cannot establish causality. Tests statistical precision under pressure.

  GoogleData ScientistStatistics & MathOnsite
• Measure Bird Species SegregationMedium

  This question evaluates spatial statistical reasoning, ecological data analysis, and the ability to formulate hypotheses, choose appropriate segregation metrics, and account for confounders and sampling bias.

  GoogleData ScientistStatistics & MathTechnical Screen
• Narrow a confidence interval for a meanMedium

  This question evaluates understanding of confidence intervals, the relationship between sample size and interval width, variance-reduction techniques, the impact of changing confidence levels, and the ability to communicate statistical risk and design-based trade-offs.

  GoogleData ScientistStatistics & MathTechnical Screen
• Prove OLS invariance to linear transformsMedium

  This question evaluates understanding of linear regression invariance under invertible linear transformations, multivariate linear algebra, and the impact of regularization (ridge and lasso) on coefficient and prediction stability in the Statistics & Math domain.

  GoogleData ScientistStatistics & MathTechnical Screen
• Reading a Regression Slope: P-values, Intervals, and CausalityMedium

  Use a fitted slope from a one-predictor regression to explain precisely what a p-value and a confidence interval mean, what assumptions they rest on, how confidence and prediction intervals differ, and why significance is not causation. Tests statistical precision under interview pressure.

  GoogleData ScientistStatistics & MathOnsite
• Test a coefficient and explain t-distributionMedium

  This question evaluates a candidate's competence in statistical inference for linear regression—specifically hypothesis testing for OLS coefficients, construction of t-statistics, estimation of standard errors, and the role of estimated error variance—within the Statistics & Math domain for Data Scientist roles.

  GoogleData ScientistStatistics & MathTechnical Screen
• Test if one value comes from N(μ,σ²)Medium

  This question tests the ability to perform statistical hypothesis testing on a single observation, evaluate the limitations of small sample sizes, and explain the trade-offs between Type I and Type II errors.

  GoogleData ScientistStatistics & MathTechnical Screen
• Understand Simpson's Paradox with Simple ExamplesMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand Simpson's Paradox with Simple Examples states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  GoogleData ScientistStatistics & MathOnsite
• Compute Parking Clustering Probability and P-ValueMedium

  This question tests your ability to calculate exact combinatorial probabilities under a null hypothesis and define a p-value for a spatial clustering test.

  GustoData ScientistStatistics & MathTechnical Screen
• Compare Candy-Color Guessing Experiments with Different Sample SizesMedium

  Evaluate and compare evidence from two binomial experiments with different sample sizes and hit rates against a random guessing null hypothesis.

  HeadlandsData ScientistStatistics & MathTechnical Screen
• t-Stat With an Added Orthogonal Regressor, Live Sharpe Check, and Absolute-Loss EstimatorMedium

  Solve three core quantitative problems covering OLS regression mechanics under orthogonal regressors, statistical significance of backtested Sharpe ratios in live deployment, and loss-function minimization for L1 error.

  HeadlandsData ScientistStatistics & MathOnline Assessment
• Choose Mean or Median for Trading Profit MetricsMedium

  This problem tests your understanding of statistical measures of central tendency, specifically how different distributions affect the choice between mean and median in high-frequency trading scenarios.

  Hudson River TradingSoftware EngineerStatistics & MathTechnical Screen
• Compare Mean and Median for a Distribution with a Point MassMedium

  This question tests the theoretical and empirical comparison between the sample mean and sample median on a mixture distribution containing a discrete point mass and a Gaussian component.

  Hudson River TradingData ScientistStatistics & MathTechnical Screen
• How do you detect and fix multicollinearity?Easy

  This question evaluates understanding of multicollinearity and related competencies in regression diagnostics, feature engineering, model interpretability, and the trade-offs between coefficient inference and predictive performance.

  IBMData ScientistStatistics & MathTechnical Screen
• Analyze Optimal Funding Allocations Across Election DistrictsHard

  Analyze election-district funding games with weak-composition counting, favorable alignments, explicit allocations, and lower-bound proofs for possible and impossible wins under fixed budgets.

  ImcQuantitative TraderStatistics & MathTechnical Screen
• Choose tests under non‑normal, unequal varianceHard

  This question tests the ability to select and justify statistical inference methods for metrics that violate standard parametric assumptions, specifically when dealing with heavy-tailed, heteroskedastic, and zero-inflated distributions.

  InstacartData ScientistStatistics & MathOnsite
• Analyze Newton-Raphson Convergence, Choose Sigma, and Derive VegaMedium

  This question evaluates numerical root-finding and option pricing competencies, specifically understanding Newton–Raphson convergence properties, implied volatility inference under the Black–Scholes model, and option sensitivity (Vega).

  Intercontinental ExchangeData ScientistStatistics & MathTechnical Screen
• Interpret Model Metrics, Regularization, and Resampling FundamentalsMedium

  This problem tests your understanding of foundational machine learning metrics for imbalanced classification and the theoretical utility of regularization and resampling techniques.

  J.P. MorganData ScientistStatistics & MathHR Screen
• Define the p-Value, Then Explain How to Test Two Price Series for CointegrationEasy

  Two statistics questions from a systematic trading screen: give a precise definition of the p-value and its common misreadings, then explain what cointegration is and how to test two price series for it. It tests hypothesis-testing rigor, stationarity, the difference between correlation and cointegration, and multiple-testing pitfalls.

  Jain GlobalData ScientistStatistics & MathTechnical Screen
• Calculate win chance in three-player dice gameMedium

  This question evaluates understanding of probability theory, conditional probability, and modelling of repeated stochastic events such as ties and rerolls, and is categorized under Statistics & Math.

  Jane StreetSoftware EngineerStatistics & MathHR Screen
• Estimate the Number of Florists in Sydney and Make a MarketMedium

  Tests Fermi estimation, sanity checking via dual perspectives (demand vs. supply/geographic), quoting a two-sided market under uncertainty, and updating beliefs given market flow or new information.

  Jane StreetQuantitative TraderStatistics & MathTechnical Screen
• Optimal Stopping with Square-Number RuinHard

  Analyze an optimal stopping game where a player rolls a fair six-sided die to accumulate points but busts if the running sum hits a positive perfect square. Tests dynamic programming, renewal theory intuition, and rigorous edge-case analysis under uncertain stopping boundaries.

  Jane StreetSoftware EngineerStatistics & MathTechnical Screen
• Optimize a Two-Box Place-or-Take GameHard

  Analyze and optimize a 100-turn, two-box stochastic game with capacity constraints and opaque states. Derive the optimal decision policy, examine boundary/edge cases, and evaluate how the strategy changes when core assumptions are relaxed.

  Jane StreetSoftware EngineerStatistics & MathTechnical Screen
• Solve probability and game-theory puzzlesEasy

  This set of puzzles evaluates probabilistic reasoning, Bayesian inference, expected-value optimization, optimal stopping, and strategic game-theoretic reasoning under uncertainty and asymmetric information, situated in the Statistics & Math domain.

  Jane StreetQuantitative TraderStatistics & MathTechnical Screen
• How to compute correlation from variancesEasy

  Practice deriving correlation from variance identities for two random variables. The solution explains why Var(X+Y) and Var(X) alone are insufficient, derives covariance and correlation when Var(Y) is known, simplifies the equal-variance case, and checks edge cases where correlation is invalid or inconsistent.

  JefferiesData ScientistStatistics & MathTechnical Screen
• How to derive correlation from variancesMedium

  Evaluates derivation of correlation from variance identities for two random variables. Strong answers explain why Var(X+Y) and Var(X) alone do not identify correlation, then derive covariance and correlation when Var(Y) is also known.

  JefferiesData ScientistStatistics & MathTechnical Screen
• Find the Largest Equivalence Class of FractionsEasy

  This problem tests your ability to handle numerical equivalence using canonical representations to avoid floating-point precision issues.

  Jump TradingSoftware EngineerStatistics & MathTechnical Screen