Statistics & Math interview questions

Showing 181–210 of 439 results

• Probability That the Sum of 1,000 Fair Die Rolls Exceeds a ThresholdHard

  A fair six-sided die is rolled 1,000 times; estimate the probability that the sum of the faces exceeds a threshold, including a threshold of 600. Tests the mean and variance of one roll, how they scale for a sum of independent rolls, the normal approximation with a continuity correction, and range checks.

  LiftoffMachine Learning EngineerStatistics & MathOnsite
• Choose better bank queue and describe distributionsEasy

  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.

  LinkedInData ScientistStatistics & MathTechnical Screen
• Compare queueing systems and common distributionsMedium

  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.

  LinkedInData ScientistStatistics & MathTechnical Screen
• Derive expectation for two consecutive headsMedium

  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.

  LinkedInData ScientistStatistics & MathTechnical Screen
• Derive mean and variance of x̄Medium

  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.

  LinkedInData ScientistStatistics & MathTechnical Screen
• Estimate an Experiment’s Mean Effect from Cohort and Metric TablesMedium

  Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.

  LinkedInData ScientistStatistics & MathOnsite
• Measure Causal Impact of Self-Selected App RedesignHard

  LinkedIn causal inference prompt on measuring a self-selected app redesign, covering potential outcomes, ATT, propensity matching or weighting, staggered difference-in-differences, event studies, covariates, balance, and pre-trend validation.

  LinkedInData ScientistStatistics & MathOnsite
• Transform Biased and Uniform Random Bits ExactlyMedium

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

  LinkedInMachine Learning EngineerStatistics & MathTechnical Screen
• Analyze Rider Behavior in Dynamic-Pricing TrialMedium

  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.

  LyftData ScientistStatistics & MathTechnical Screen
• Commuter Ride-Choice Probability: Committed and Permanent RidersMedium

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

  LyftData ScientistStatistics & MathTechnical Screen
• Compute Commuter Ride ProbabilitiesMedium

  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.

  LyftData ScientistStatistics & MathTechnical Screen
• Compute Poisson supply–demand match probabilityMedium

  This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.

  LyftData ScientistStatistics & MathOnsite
• Determine Probability of Single Ride on Following DayEasy

  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.

  LyftData ScientistStatistics & MathTechnical Screen
• Explain Bayes’ Theorem and P-Value in Decision-MakingEasy

  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.

  LyftData ScientistStatistics & MathTechnical Screen
• Calculate tiered cost and break-evenMedium

  This question evaluates the ability to model tiered piecewise cost functions, compute total and average unit costs, and perform break-even analysis using basic algebra and arithmetic.

  MathWorksSoftware EngineerStatistics & MathOnline Assessment
• Compute probability of drawing redsEasy

  This question evaluates understanding of probability theory and combinatorics, specifically reasoning about sampling without replacement and calculating event probabilities using counting methods.

  MathWorksSoftware EngineerStatistics & MathOnline Assessment
• Compute urn-draw probabilities and expectationsEasy

  This question evaluates understanding of elementary probability concepts—dependent events, conditional probability, and expected value—using an urn model with draws without replacement.

  MathWorksSoftware EngineerStatistics & MathOnline Assessment
• Deduce position from logic cluesHard

  This question evaluates the ability to model and reason about constraint-satisfaction on a 2D grid, including logical inference with directional and adjacency relations, combinatorial placement constraints, and determination of uniqueness.

  MathWorksSoftware EngineerStatistics & MathOnline Assessment
• Deduce row order from logic cluesEasy

  This question evaluates logical deduction, constraint satisfaction, and basic combinatorial reasoning used to infer ordering from positional clues. Commonly asked to assess a candidate's ability to apply discrete math and spatial reasoning within the Statistics & Math domain, it targets conceptual understanding of constraints rather than practical implementation.

  MathWorksSoftware EngineerStatistics & MathOnline Assessment
• Find the Probability That One Shop Supplies the Top Two CoffeesHard

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

  MercorSoftware EngineerStatistics & MathTechnical Screen
• Probability Both Drinks from One Coffee Shop Outscore Both Drinks from AnotherMedium

  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.

  MercorSoftware EngineerStatistics & MathTechnical Screen
• Probability That One Customer's Two Coffees Beat Another's TwoMedium

  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.

  MercorSoftware EngineerStatistics & MathTechnical Screen
• Reason About Coffee-Quality Comparison ProbabilitiesMedium

  Calculate conditional comparison probabilities for coffee quality scores under continuous and discrete distributions, accounting for ties and sample permutations.

  MercorSoftware EngineerStatistics & MathTechnical Screen
• Analyze User Comment Distribution and Sampling EffectsMedium

  Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.

  MetaData ScientistStatistics & MathOnsite
• Analyze User Transfer Distribution in Initial Launch PeriodMedium

  Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.

  MetaData ScientistStatistics & MathOnsite
• Analyze ad targeting expectations and distributionsMedium

  This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.

  MetaData ScientistStatistics & MathHR Screen
• Analyze skewed comments and sampling effectsMedium

  This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.

  MetaData ScientistStatistics & MathOnsite
• Annotating and forecasting a long‑tail distributionMedium

  Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.

  MetaData ScientistStatistics & MathOnsite
• Apply sequential testing without p-hackingHard

  This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.

  MetaData ScientistStatistics & MathOnsite
• Calculate Ad Insertion Statistics for Two MethodsMedium

  Meta statistics prompt comparing two ad-insertion strategies, covering binomial moments, deterministic quotas, tail probability for excessive ads, adjacent ad pairs, and risk of long ad runs.

  MetaData ScientistStatistics & MathOnsite