Statistics & Math Interview Questions

Showing 91–120 of 218 results

• GoogleMedium·Data Scientist
  Test a coefficient and explain t-distribution

  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.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Test if one value comes from N(μ,σ²)

  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.

  Statistics & MathTechnical ScreenStatisticsHypothesis TestingNormal Distribution
• GustoMedium·Data Scientist
  Compute Parking Clustering Probability and P-Value

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

  Statistics & MathTechnical ScreenProbabilityCombinatoricsHypothesis Testing
• HHeadlandsMedium·Data Scientist
  Compare Candy-Color Guessing Experiments with Different Sample Sizes

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

  Statistics & MathTechnical Screenstatisticshypothesis-testingbinomial-distribution
• Hudson River TradingMedium·Software Engineer
  Choose Mean or Median for Trading Profit Metrics

  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.

  Statistics & MathTechnical ScreenStatisticsFinanceData Analysis
• Hudson River TradingMedium·Data Scientist
  Compare Mean and Median for a Distribution with a Point Mass

  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.

  Statistics & MathTechnical Screenstatisticsprobabilityestimation
• IBMEasy·Data Scientist
  How do you detect and fix multicollinearity?

  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.

  Statistics & MathTechnical Screen
• ImcHard·Quantitative Trader
  Analyze Optimal Funding Allocations Across Election Districts

  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.

  Statistics & MathTechnical Screen
• IEIntercontinental ExchangeMedium·Data Scientist
  Analyze Newton-Raphson Convergence, Choose Sigma, and Derive Vega

  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).

  Statistics & MathTechnical Screen
• JGJain GlobalEasy·Data Scientist
  Define the p-Value, Then Explain How to Test Two Price Series for Cointegration

  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.

  Statistics & MathTechnical Screen
• Jane StreetMedium·Quantitative Trader
  Estimate the Number of Florists in Sydney and Make a Market

  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.

  Statistics & MathTechnical ScreenFermi EstimationMarket MakingProbability
• Jane StreetHard·Software Engineer
  Optimal Stopping with Square-Number Ruin

  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.

  Statistics & MathTechnical Screenprobabilityoptimal-stoppingdynamic-programming
• Jane StreetHard·Software Engineer
  Optimize a Two-Box Place-or-Take Game

  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.

  Statistics & MathTechnical ScreenProbabilityDynamic ProgrammingMarkov Decision Process
• Jane StreetEasy·Quantitative Trader
  Solve probability and game-theory puzzles

  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.

  Statistics & MathTechnical Screen
• JJefferiesEasy·Data Scientist
  How to compute correlation from variances

  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.

  Statistics & MathTechnical Screen
• JJefferiesMedium·Data Scientist
  How to derive correlation from variances

  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.

  Statistics & MathTechnical Screen
• JTJump TradingEasy·Software Engineer
  Find the Largest Equivalence Class of Fractions

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

  Statistics & MathTechnical ScreenMathHash MapNumber Theory
• LinkedInEasy·Data Scientist
  Choose better bank queue and describe distributions

  This question tests your ability to apply basic queueing theory to compare waiting times and your ability to reason about how distributional shape affects central tendency measures.

  Statistics & MathTechnical ScreenQueueing TheoryProbabilityStatistics
• LinkedInMedium·Data Scientist
  Compare queueing systems and common distributions

  LinkedIn data scientist statistics screen covering pooled versus separate queues, height distributions and mixture distributions, right-skewed network degree, mean-median-mode ordering, and L1/L2 regularization bias.

  Statistics & MathTechnical Screen
• LinkedInMedium·Data Scientist
  Derive expectation for two consecutive heads

  This question evaluates mastery of probability theory and stochastic processes, specifically expectation and variance calculations, Markov-chain or recurrence reasoning, and pattern waiting times in Bernoulli trials relevant for a Data Scientist role.

  Statistics & MathTechnical Screen
• LinkedInMedium·Data Scientist
  Derive mean and variance of x̄

  This question tests your ability to derive the expected value and variance of the sample mean for a sequence of random variables, accounting for the effect of correlation between them.

  Statistics & MathTechnical ScreenStatisticsVarianceExpectation
• LinkedInMedium·Machine Learning Engineer
  Transform Biased and Uniform Random Bits Exactly

  Tests the ability to design and mathematically prove exact random variate generators between biased and uniform bit sources, analyzing termination and expected sample complexity.

  Statistics & MathTechnical ScreenProbabilityAlgorithmsSampling
• LyftMedium·Data Scientist
  Analyze Rider Behavior in Dynamic-Pricing Trial

  Analyze Rider Behavior in Dynamic-Pricing Trial 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
• LyftMedium·Data Scientist
  Commuter Ride-Choice Probability: Committed and Permanent Riders

  Tests discrete probability modeling, complementary counting, and Bayesian updating to analyze multi-day commuter behavior across distinct rider segments.

  Statistics & MathTechnical Screenprobabilitybayes-theoremconditional-probability
• LyftMedium·Data Scientist
  Compute Commuter Ride Probabilities

  This question tests the ability to apply Bayes' Theorem and marginal probability to model commuter behavior and predict ride-hailing demand based on environmental factors.

  Statistics & MathTechnical ScreenProbabilityBayesian InferenceConditional Probability
• LyftEasy·Data Scientist
  Determine Probability of Single Ride on Following Day

  Evaluates a binomial probability setup for a ride-pricing experiment with two independent offers. Strong answers model the number of reasonable prices as Binomial(2, P), compute exactly one success as 2P(1-P), and compute permanent-rider probability as P squared.

  Statistics & MathTechnical Screen
• LyftEasy·Data Scientist
  Explain Bayes’ Theorem and P-Value in Decision-Making

  Explain Bayes’ Theorem and P-Value in Decision-Making 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
• MercorHard·Software Engineer
  Find the Probability That One Shop Supplies the Top Two Coffees

  Calculate the probability that the top-ranked coffees in a blind competition come from the same shop under an exchangeable, tie-free ranking model.

  Statistics & MathTechnical Screenprobabilitycombinatoricsexchangeability
• MercorMedium·Software Engineer
  Probability Both Drinks from One Coffee Shop Outscore Both Drinks from Another

  A probability brainteaser: two drinks are ordered at each of two coffee shops and every drink receives a quality score, and you must find the probability that both drinks from the first shop outscore both drinks from the second. It tests symmetry arguments, counting rankings, and stating independence and tie assumptions.

  Statistics & MathTechnical Screen
• MercorMedium·Software Engineer
  Probability That One Customer's Two Coffees Beat Another's Two

  A probability puzzle in which two customers each buy two cups of coffee of random quality, and you find the chance that one customer's cups are better than the other's. It tests clarifying what better means, using symmetry over rankings, checking the result a second way, and reasoning about ties and correlated cups.

  Statistics & MathTechnical Screen