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Statistics & Math Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 211–240 of 439 results
- MetaEasyData ScientistCalculate Expected Impressions and Probability for Users
Meta probability question on random ad-impression allocation, including binomial expectation, probability a user sees at least one impression, expected unique reach, and large-population approximations.
Statistics & MathOnsite - MetaEasyData ScientistCalculate Posterior Fraud Probability Using Bayes' Theorem
Calculate Posterior Fraud Probability Using Bayes' Theorem 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 & MathOnsite - MetaEasyData ScientistCalculate Posterior Probability of Flagged User Being Bad Actor
Evaluates Bayesian inference and confusion-matrix reasoning for abuse detection. Strong answers derive posterior precision from prevalence, TPR, and FPR, define Type I and Type II errors, and compute true positives and false positives for 10 million users.
Statistics & MathOnsite - MetaEasyData ScientistCalculate Probability of Honest and Relevant Chatbot Answers
Calculate Probability of Honest and Relevant Chatbot Answers 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 & MathOnsite - MetaMediumData ScientistCharacterize and compare transfer-count distributions over time
This question evaluates a candidate's ability to model zero-inflated, heavy-tailed count data, interpret distributional summaries and percentiles, reason about temporal shifts from retention and fraud dynamics, and identify robust summary statistics and diagnostics for a Data Scientist role.
Statistics & MathOnsite - MetaMediumData ScientistCharacterize metric distribution and quantiles
This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.
Statistics & MathOnsite - MetaEasyData ScientistCompare Ad-Insertion Strategies: Expected Ads and Probabilities
Evaluates probability, expectation, and variance for newsfeed ad-insertion strategies. Strong answers compute expected ad load, at-least-one exposure probability, and explain why equal expected counts can still produce different user experiences.
Statistics & MathOnsite - MetaMediumData ScientistCompare Bayesian and frequentist decisions
This question evaluates understanding of Bayesian inference versus frequentist hypothesis testing, decision theory under asymmetric loss, sequential monitoring/optional stopping, and prior elicitation applied to A/B testing within the Statistics & Math domain.
Statistics & MathOnsite - MetaEasyData ScientistCompute Bayes probability for fake accounts
This question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.
Statistics & MathOnsiteBayes TheoremProbabilityStatistics - MetaEasySoftware EngineerCompute an Overnight Alarm Interval
This problem tests the ability to calculate time intervals across date rollovers by converting 12-hour clock times into a continuous 24-hour linear timeline.
Statistics & MathHR ScreenMathDate-TimeAlgorithms - MetaMediumData ScientistCompute and correct correlation significance inflation
This question tests the understanding of how multiple hypothesis testing increases the risk of false positives (Type I error) in high-dimensional correlation matrices and requires the application of correction methods to maintain statistical integrity.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingCorrelation - MetaMediumData ScientistCompute fraud probabilities with Bayes and Binomial
This question evaluates a candidate's understanding of probabilistic modeling and statistical decision-making, focusing on the Binomial distribution for session-level events and Bayes' theorem for posterior probabilities in a fraud-detection setting.
Statistics & MathOnsite - MetaHardData ScientistCompute p-values, power, and adjust errors
This question evaluates statistical reasoning and applied inference skills including hypothesis testing for proportions with sequential monitoring, multiple-testing control (Benjamini–Hochberg vs Bonferroni), Bayesian posterior estimation for classification flags, and robust AOV estimation and transformation techniques.
Statistics & MathOnsite - MetaMediumData ScientistCompute posterior and event counts in fraud screen
This question evaluates a candidate's competence in probability and statistical inference, covering binomial probability calculations, Bayes' theorem for posterior probability, rare-event base-rate reasoning, and modeling of correlated binary signals in a fraud-screening context.
Statistics & MathOnsite - MetaMediumData ScientistCompute posterior fake probability using Bayes' rule
The problem evaluates understanding of Bayes' rule and conditional probability within the Statistics & Math domain for a Data Scientist role, at an introductory applied probability/interpretation level.
Statistics & MathTechnical Screen - MetaEasyData ScientistCompute probabilities for chatbot response quality
This question evaluates a candidate's grasp of basic probability concepts, including independence, joint event probabilities, and i.i.d. assumptions. It is commonly asked to test probabilistic reasoning about repeated trials within the Statistics & Math domain and is primarily a conceptual understanding problem with a simple practical application.
Statistics & MathOnsite - MetaEasyData ScientistCompute probability an account is fake
This question tests the ability to apply Bayes' Theorem to update the probability of a hypothesis (an account being fake) given new evidence (an automated flag).
Statistics & MathTechnical ScreenBayesian StatisticsConditional ProbabilityClassification Metrics - MetaHardData ScientistCompute sample size and test duration correctly
This question evaluates competency in experiment design and statistical power calculations, including sample size estimation, variance misspecification effects, multiple-comparison corrections, sequential monitoring boundaries, and adjustments for covariates or clustering.
Statistics & MathTechnical Screen - MetaMediumData ScientistConstruct a 95% Confidence Interval for Comment Counts
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaMediumSoftware EngineerDerive max distinct frequencies for n items
The question evaluates combinatorial reasoning, discrete mathematics, and constructive extremal analysis by asking for the maximum number of distinct frequency counts in an array and a matching construction.
Statistics & MathTechnical Screen - MetaMediumData ScientistDesign Sampling Strategy to Estimate Fake News Proportion
Design Sampling Strategy to Estimate Fake News Proportion 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 & MathOnsite - MetaEasyData ScientistDetermine Posterior Probability of Bad User Prediction
Meta Bayes theorem question on a bad-actor classifier, covering prevalence, sensitivity, specificity, false positives, positive predictive value, and the base-rate effect.
Statistics & MathOnsite - MetaMediumData ScientistDetermine Probability of Fourth Good Response After Three Successes
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Probability of Fourth Good Response After Three Successes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaEasyData ScientistDetermine Probability of Friend Request Being Fake
Determine Probability of Friend Request Being Fake 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 & MathOnsite - MetaHardData ScientistEstimate CTR lift with binomial tests and errors
This question evaluates statistical inference for proportions, sequential monitoring, and multiple-comparison error control—assessing competence with binomial-based CTR comparison, pooled standard errors, z-tests and confidence intervals, Type I error inflation from peeking, and FWER/FDR considerations.
Statistics & MathOnsite - MetaMediumData ScientistEstimate Lift and Significance in Facebook Ad Campaigns
Meta statistics prompt on Facebook ad conversion lift, covering absolute and relative lift, two-proportion tests, confidence intervals, power for 5% relative lift, and Bayesian beta-binomial reframing.
Statistics & MathOnsite - MetaMediumData ScientistEstimate Portal’s causal lift on video-call usage
This problem tests the ability to design an observational causal inference framework using Difference-in-Differences (DiD) in a setting with staggered adoption, specifically addressing selection bias and household-level measurement errors.
Statistics & MathTechnical ScreenCausal InferenceDifference-in-DifferencesStaggered Adoption - MetaHardData ScientistEstimate and validate weights for engagement actions
This question tests the ability to derive an engagement scoring model, handle high-dimensional collinear features, and establish a robust validation framework for point-in-time predictions.
Statistics & MathTechnical ScreenRegressionRegularizationMulticollinearity - MetaMediumData ScientistEstimate bots and CI from DAU spike
This question tests the ability to decompose a mixture of legitimate user traffic and synthetic bot/CI traffic using statistical modeling, and requires justifying the choice of distribution for overdispersed count data.
Statistics & MathOnsiteMixture ModelsNegative BinomialBootstrapping - MetaMediumData ScientistEstimate fake-account prevalence with capture-recapture
This question tests the ability to apply the Lincoln-Petersen estimator to estimate a hidden population size and addresses the limitations of the assumptions underlying capture-recapture methods.
Statistics & MathOnsiteStatisticsEstimationInference
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