Statistics & Math interview questions

Showing 361–390 of 439 results

• Contrast FCF vs NI; choose one statementMedium

  This question evaluates financial statement analysis skills—specifically reconciling net income to free cash flow, identifying accounting and working-capital red flags, and selecting which single statement best informs credit risk, profitability trends, and cash runway, thereby testing valuation, solvency, and financial-modeling competency in the Statistics & Math domain. It is commonly asked because interviewers probe both conceptual understanding and practical application of accrual versus cash accounting, the effects of non‑cash charges, capex and working‑capital movements on valuation, and awareness of which metrics (for example FCF margin, interest coverage, and current ratio) become unreliable or unobtainable when only one statement is available.

  SnowflakeData ScientistStatistics & MathTechnical Screen
• Answer Statistics Questions for Spokeo DS Role QuizMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Answer Statistics Questions for Spokeo DS Role Quiz states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  SpokeoData ScientistStatistics & MathOnsite
• Compare Bonds, Common Equity, and Dividend PaymentsMedium

  Compare debt and common equity claims, cash flows, risks, and why company profits do not guarantee common-share dividends.

  SquarepointData ScientistStatistics & MathTechnical Screen
• Compare Winning Chances in a Card Game That Replays TiesMedium

  Use symmetry and tied-round replay to compare two players drawing cards without replacement and derive the game's eventual winning probabilities.

  SquarepointData ScientistStatistics & MathTechnical Screen
• Estimate Market Beta from Aligned Asset and Benchmark ReturnsMedium

  Define market beta, identify the benchmark data needed beyond a stock's returns, and explain regression estimation and uncertainty.

  SquarepointData ScientistStatistics & MathTechnical Screen
• Explain derivatives, YTM, and core statisticsMedium

  This problem tests your understanding of fundamental financial math concepts (derivatives and YTM) and your ability to apply statistical rigor when modeling financial data.

  SquarepointData ScientistStatistics & MathTechnical Screen
• Find the Correlation Between Two Die-Face CountsMedium

  A fair six-sided die is rolled independently `n` times, where `n` is a fixed positive integer. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.

  SquarepointSoftware EngineerStatistics & MathTechnical Screen
• Find the Probability That Two Uniform Variables Have Product Above One-HalfMedium

  Compute the probability that two independent uniform variables have product above one-half by conditioning or integrating over the unit square.

  SquarepointData ScientistStatistics & MathTechnical Screen
• Calculate conditional probabilities for coin tossesEasy

  This question evaluates understanding of conditional probability and independence in simple probabilistic experiments. It belongs to the Statistics & Math domain, is commonly asked to assess foundational probabilistic reasoning used in statistical inference and algorithmic decision-making, and targets conceptual understanding with elementary practical computation.

  StackAdaptSoftware EngineerStatistics & MathHR Screen
• Camel and Bananas: Maximize Bananas Delivered Across the DesertMedium

  Classic camel-and-bananas brainteaser: a camel that carries at most 1,000 bananas and eats one per kilometer must move 3,000 bananas to a market 1,000 km away. Find the maximum number that can arrive, testing piecewise cost reasoning, depot planning, and a proof of optimality.

  StackAdaptMachine Learning EngineerStatistics & MathOnsite
• Explain Why a Geometric Mean Can Exceed Its MedianEasy

  Tests derivation and comparison of the expected value and median of a geometric distribution, explaining how right-skewness drives the mean above the median across different parameterizations.

  StackAdaptMachine Learning EngineerStatistics & MathTechnical Screen
• Reservoir Sampling: Uniform k-of-n Sample From a Stream of Unknown LengthMedium

  Design a one-pass algorithm that keeps a uniform random sample of k items from a stream whose length is unknown in advance, using memory proportional to k. Tests the replacement rule, an inductive proof that every item is kept with equal probability, complexity, and edge cases.

  StackAdaptMachine Learning EngineerStatistics & MathOnsite
• Choose threshold under costs and uncertaintyMedium

  This question evaluates cost-sensitive threshold selection, uncertainty quantification for expected profit, calibration diagnostics, and population drift detection in a data science setting.

  StripeData ScientistStatistics & MathTechnical Screen
• Compute power and interpret uplift metricsMedium

  This question evaluates a Data Scientist's practical and conceptual competencies in A/B test design and analysis within the Statistics & Math domain, covering power/sample-size calculations, binary outcome inference, CUPED variance reduction, multiple-testing adjustments, and handling clustered/geographical data.

  StripeData ScientistStatistics & MathTechnical Screen
• Count Open Lockers After 100 RoundsMedium

  This problem tests mathematical reasoning and pattern recognition, specifically the relationship between integer divisors and toggling operations.

  TekionSoftware EngineerStatistics & MathTechnical Screen
• Find an Optimal Strategy for an Even-Row Coin GameMedium

  Analyze optimal play in an even-row coin game, using position parity and game values to distinguish a guaranteed tie from a strict first-player win.

  TekionSoftware EngineerStatistics & MathTechnical Screen
• Explain and derive importance sampling estimatorsHard

  This question evaluates understanding of importance sampling, Monte Carlo estimators, weight normalization, variance behavior, optimal proposal selection, and effective sample size, assessing competency in statistical estimation and Monte Carlo methods.

  TeslaMachine Learning EngineerStatistics & MathTechnical Screen
• Test regional response-rate differences rigorouslyMedium

  This question evaluates applied statistical modeling and causal-inference competencies, including adjustment for confounding, analysis of proportion/count outcomes, variance estimation under clustering, multiple-comparison control, diagnostic detection of Simpson’s paradox, and clear effect-size reporting; it is categorized under Statistics & Math and emphasizes practical application with necessary conceptual understanding. Such problems are commonly asked to assess the ability to justify and interpret comparative analyses that account for covariate mix, temporal structure, and multiple-testing concerns, and this summary is in English.

  ThumbtackData ScientistStatistics & MathOnsite
• Act when A/B result is not significantHard

  This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.

  TikTokData ScientistStatistics & MathOnsite
• Calculate Expected Draws for X > 0.8 in Uniform(0,1)Easy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Draws for X > 0.8 in Uniform(0,1) states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokData ScientistStatistics & MathTechnical Screen
• Compute cluster-aware significance and sequential correctionsMedium

  This question evaluates competency in clustered randomized experiment analysis, including calculation of design effect and effective sample size, cluster-robust inference for differences in proportions, sequential alpha spending (O’Brien–Fleming-style) and comparisons with Bonferroni, Holm–Bonferroni adjustments for multiple guardrail metrics, and Bayesian ROPE interpretation. It is in the Statistics & Math domain and is commonly asked to probe how candidates handle intra-cluster correlation, control Type I error across interim looks and multiple metrics, and demonstrate both conceptual understanding and practical application of power, duration, and multiplicity trade-offs.

  TikTokData ScientistStatistics & MathHR Screen
• Control confounding in observational ad liftHard

  This question evaluates mastery of observational causal inference for ATE estimation, covering causal DAGs and identification, pre-treatment adjustment, propensity-score and inverse-probability/doubly-robust estimation, diagnostic checks, sensitivity analysis for unobserved confounding, and variance/uncertainty quantification in the context of ad exposure data. It is commonly asked because real-world advertising analyses cannot rely on randomization and interviewers need assurance of both conceptual understanding of identification assumptions and practical application of estimation and diagnostic techniques; the category is Statistics & Math and the level of abstraction spans conceptual reasoning and applied implementation.

  TikTokData ScientistStatistics & MathOnsite
• Derive L1 vs L2 effects with correlationMedium

  This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.

  TikTokData ScientistStatistics & MathTechnical Screen
• Test Billboard Campaign Conversion Rate Exceeds 60%Easy

  Evaluates one-sample proportion testing for a billboard conversion rate with 65 conversions out of 100 users. Strong answers state one-sided hypotheses, compute the standard error and confidence interval, run the correct z test against 60%, and interpret why statistical evidence may be insufficient.

  TikTokData ScientistStatistics & MathOnsite
• Use DiD for staggered treatment adoptionHard

  This question evaluates proficiency in causal inference for panel data, specifically difference-in-differences with staggered adoption, heterogeneous and dynamic treatment effects, event-study design, pre-trend diagnostics, cluster-robust inference, and comparison with propensity-score matching.

  TikTokData ScientistStatistics & MathTechnical Screen
• Answer four core statistics questionsEasy

  This multipart problem evaluates competency in probability and mathematical statistics, covering order statistics and covariance, the rationale for degrees of freedom in sample variance, z-test sample size calculation, and a one-sided concentration inequality, categorized under Statistics & Math for a Data Scientist role and combining conceptual derivations with practical formula application. Such questions are commonly asked to verify mathematical rigor in deriving expectations and covariances, understanding sampling distributions and unbiased estimation, applying hypothesis testing power formulas, and proving probabilistic inequalities that underpin statistical reasoning.

  Two SigmaData ScientistStatistics & MathTechnical Screen
• Explain why the t-statistic helpsHard

  This question evaluates understanding of the t-statistic, standardized effect sizes, standard errors, hypothesis testing, and interpretation of statistical evidence within the Statistics & Math domain, and it targets conceptual understanding with practical application implications.

  Two SigmaData ScientistStatistics & MathTechnical Screen
• Why Use a t-Statistic?Hard

  This problem tests your conceptual understanding of the t-statistic, specifically how sample parameters influence its value and how it bridges the gap between estimation and hypothesis testing.

  Two SigmaData ScientistStatistics & MathTechnical Screen
• Why is the t-statistic usefulEasy

  This question tests the candidate's conceptual understanding of the t-statistic, specifically its role in hypothesis testing when population parameters are unknown.

  Two SigmaData ScientistStatistics & MathTechnical Screen
• Why use the t-statistic?Hard

  This question tests the conceptual understanding of the t-statistic, specifically why it is used instead of the Z-statistic when population parameters are unknown and how it accounts for uncertainty in sample variance.

  Two SigmaData ScientistStatistics & MathTechnical Screen