Topic
Statistics & Math interview questions
439 Statistics & Math questions from 87 companies, with hints, runnable code and solution walkthroughs.
119 easy239 medium81 hard
Statistics & Math interview questions by the numbers
Last updated
Katkat has 439 Statistics & Math interview questions from 87 companies; the most come from Meta (11%), Capital One (9%) and SIG (Susquehanna) (8%). By difficulty: 27% easy, 54% medium and 18% hard. The most common round is Technical Screen (50%). Frequent concepts include Statistics, Probability, Inference and Hypothesis Testing.
Companies (87)
- Meta11% · 47
- Capital One9% · 39
- SIG (Susquehanna)8% · 33
- Google7% · 32
- Optiver4% · 19
- Amazon3% · 15
- Upstart3% · 13
- DRW3% · 12
Difficulty
- Easy27% · 119
- Medium54% · 239
- Hard18% · 81
Interview rounds
- Technical Screen50% · 218
- Onsite28% · 124
- Online Assessment18% · 78
- HR Screen4% · 18
- Other0% · 1
Roles
- Data Scientist78% · 344
- Software Engineer10% · 43
- Quantitative Researcher6% · 26
- Quantitative Trader3% · 14
- Machine Learning Engineer2% · 10
Frequent concepts
- Statistics14% · 61
- Probability11% · 49
- Inference3% · 15
- Hypothesis Testing3% · 13
- probability3% · 13
- statistics3% · 11
Most mentioned in interview experiences
- 1.Choose Classification Metrics Under Asymmetric Costs2 reports
- 2.Compare queueing systems and common distributions2 reports
- 3.Compute decay, OLS, and classic probability results2 reports
- 4.Explain P-value, Confidence Interval, and Multiple Testing Adjustments2 reports
- 5.Perform no-calculator math accurately and fast2 reports
- 6.Probability That One Customer's Two Coffees Beat Another's Two2 reports
- 7.Solve core probability/statistics mini-problems2 reports
- 8.Analyze OLS After Duplicating Every Observation1 report
- 9.Analyze Optimal Funding Allocations Across Election Districts1 report
- 10.Analyze skewed comments and sampling effects1 report
Counted from 97 candidate interview experiences on Katkat. How we collect and count
Showing 211–240 of 439 results
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.
MetaData ScientistStatistics & MathOnsiteCalculate 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.
MetaData ScientistStatistics & MathOnsiteEvaluates 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.
MetaData ScientistStatistics & MathOnsiteCalculate 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteEvaluates 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteThis question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.
MetaData ScientistStatistics & MathOnsiteThis problem tests the ability to calculate time intervals across date rollovers by converting 12-hour clock times into a continuous 24-hour linear timeline.
MetaSoftware EngineerStatistics & MathHR ScreenThis 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.
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteThe 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.
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathOnsiteThis 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).
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathOnsiteThe 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.
MetaSoftware EngineerStatistics & MathTechnical ScreenDesign 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.
MetaData ScientistStatistics & MathOnsiteMeta Bayes theorem question on a bad-actor classifier, covering prevalence, sensitivity, specificity, false positives, positive predictive value, and the base-rate effect.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteDetermine 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsiteMeta 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathTechnical ScreenThis 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.
MetaData ScientistStatistics & MathOnsiteThis 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.
MetaData ScientistStatistics & MathOnsite