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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"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."

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"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."

"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."

"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."

"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."

"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
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)...
Diagnose a non-significant experiment outcome
A/B Test Interpretation, Power, and Decision-Making Under Asymmetric Loss Context You ran a two-sample A/B test on a primary mean metric (two-sided t-...
Quantify launch decision with tests and guardrails
You will formalize the statistical decision rules for the Instagram button experiment described above. Given: baseline exploration rate (p0) = 0.15 pe...
Derive no-click probability and sketch implications
Click Probability Across Repeated Impressions Context: We show A impressions of the same item to a user. Unless otherwise stated, each impression is a...
Estimate fake-account prevalence with capture-recapture
Capture–Recapture Estimation with Two Detectors You are evaluating suspected fake accounts on a platform with 50 million active accounts. In one week:...
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...
Calculate Ad Insertion Statistics for Two Methods
Scenario Evaluating two ad-insertion strategies in a 100-post feed. - Option A (independent placement): Each post independently becomes an ad with pro...
Choose Randomization Unit and Mitigate Network Effects
A/B Test Design for a New Messenger Feature with Network Effects Context You are designing an A/B test for a new Messenger feature in a social messagi...
Calculate Expected Comments and Confidence Interval Analysis
Scenario You are analyzing the distribution of comment counts per post for a social platform feature over a fixed time window. Let X be the random var...
Analyze Central Limit Theorem in User Comment Distribution
Comments per User — CLT, Expectation, SD, and 95% CI Context You are measuring how many comments each user makes in a fixed time window (e.g., one wee...
Calculate Expected Impressions and Probability for Users
Random Allocation of Y Ad Impressions to X Users Setup (assumptions) - There are X distinct users and Y ad impressions. - Each impression is assigned ...
Calculate Posterior Probability of Flagged User Being Bad Actor
Bayesian inference for abuse detection with error control Setup A platform runs a binary classifier that flags users who might be bad actors. Let: - p...
Estimate Lift and Significance in Facebook Ad Campaigns
Measuring Conversion Lift from Facebook Ads Scenario An advertiser is running a randomized experiment on Facebook. Users are split into: - Control (un...
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...
Compare first-score vs all-scores estimators
You have two candidate estimators for survey quality based on the score column over 2025-08-26 to 2025-09-01: - E_first: For each user×survey pair, ta...
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 posterior for accurate-but-rare classifier
Bayes' Theorem: Interpreting Screening Model Predictions Context You are evaluating a binary screening model that flags "bad" users in a population. T...
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...
Model comment counts and detect anomalies
Modeling Heavy-Tailed Comment Counts and Robust Monitoring You are analyzing daily comment counts at the post–day level. The distribution is heavy-tai...
Describe Facebook User Comment Distribution Shape and Justification
Characterizing Comments per User on Facebook Context You are analyzing the number of comments made by each user over a fixed time window (e.g., 30 day...