Meta Statistics & Math Interview Questions
Meta Statistics & Math interview questions at Meta emphasize clear statistical judgment applied to product problems rather than rote formula recall. Interviewers typically probe experimental design, hypothesis testing, confidence intervals, power and sample-size thinking, probability and distribution intuition, and practical trade-offs at scale. What’s distinctive is the expectation that you tie statistical conclusions to product impact: show how uncertainty, effect size, seasonality, and clustering would affect a recommendation, and how you’d instrument guardrails to prevent harm. Expect a mix of brainteasers, short analytical problems, and open-ended experiment or metric-diagnosis cases that require both math and product sense. For interview preparation, focus on fundamentals (CLT, t-tests, p-values vs effect size, Bayes basics) and practice translating results into decisions. Work timed problems that include A/B design, power calculations, and conditional probability, and rehearse explaining assumptions and limitations concisely. Use mock interviews to sharpen verbalization of uncertainty and trade-offs, and prepare examples where you diagnosed noisy metrics or redesigned experiments—Meta favors candidates who demonstrate sound statistics and pragmatic product thinking.

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Fake Accounts [AE]
Detecting and Managing Bad Accounts on a Social Platform 1) Probability of a Bad Account Sending Friend Requests Context: 1% of accounts are bad. Bad ...
Compute probability an account is fake
A platform uses an automated classifier to flag potentially fake accounts. Assume: - Base rate: 2% of accounts are fake. - The classifier flags a fake...
Compute fraud probabilities with Bayes and Binomial
Fake-Account Detection with Binomial Sessions and Bayes Updating You are evaluating a rules-based detector for fake accounts on an online platform. Ea...
Model user-level ad impression allocation
Random Assignment of Ad Impressions to Users Context - There are X distinct users and Y ad impressions (X ≥ 1, Y ≥ 0 integers). - Each impression is i...
Apply sequential testing without p-hacking
Sequential Monitoring With Early Stopping Context: You are planning a two‑sided hypothesis test with continuous monitoring and early stopping for effi...
Compute Bayes probability for fake accounts
A platform is trying to detect fake accounts. Assume: - Base rate of fake accounts is \(P(F)=p\). - A detection system flags an account as suspicious ...
Estimate variance for ratio metrics
KPI Variance via Delta Method and Inference Choices for ARPU Context You run experiments where each arm produces aggregate totals per analysis unit (e...
Compute posterior and event counts in fraud screen
Fake-Account Screening with Threshold on 5 Signals You are designing a rule-based screener that flags an account if at least k of 5 binary signals fir...
Analyze User Comment Distribution and Sampling Effects
Scenario You are analyzing daily comment counts per user. The per-user distribution of counts is right-skewed (many zeros/low counts and a long right ...
Estimate bots and CI from DAU spike
Mixture Spike and Mean-Difference Inference for Daily Comments Context A product has DAU (daily active users) = 2,000,000. On day T, total comments in...
Compute sample size and test duration
You will run a two-arm A/B test on a signup funnel. Given: baseline conversion p0 = 4.0%; you care about detecting a 10% relative uplift (p1 = 4.4%); ...
Characterize and compare transfer-count distributions over time
P2P Transfer Counts in First 30 Days: Distribution, Summaries, and Evolution Context: For a new user cohort, define X as each user’s number of peer-to...
Analyze ad targeting expectations and distributions
Ads Profit, Variance Decomposition, and Exponential Timing Context: You run an ad slot with two user segments. On each eligible page view (impression ...
Derive expected meetings given nonempty room
Zero-Truncated Binomial: Random Room Assignment Setup - There are N rooms labeled 1, 2, ..., N. - K meetings are scheduled; each meeting independently...
Compute posterior fake probability using Bayes' rule
A platform runs an automated detector to flag fake accounts. - Prior probability an account is fake: \(P(F)=0.02\). - True positive rate (sensitivity)...
Test two models' proportions for significance
Two search models, A and B, were each used once by 100 distinct users (one query per user). Success is defined per query by your composite metric (suc...
Compute sample size and test duration correctly
Powering Two Online Experiments: Sample Size, Duration, and Design Defenses You are designing experiments to improve a friend-accept rate metric in a ...
Compute conditional occupancy across two rooms
Probability and Bayes Update: Two Rooms Setup There are two rooms. Prior over occupancy states: - With probability 1/3: both rooms are occupied. - Wit...
Estimate CTR lift with binomial tests and errors
A/B Test Inference, Peeking, and Multiple Comparisons You run a two-arm A/B test of click-through rate (CTR). - Control: n_c = 10,000,000 impressions,...
Derive max distinct frequencies for n items
Maximum Number of Distinct Frequency Counts in an Array Context You are given an array of length n ≥ 1 whose elements are arbitrary integers (values m...