Tiktok Statistics & Math Interview Questions
This short guide focuses on TikTok Statistics & Math interview questions and explains what makes them distinctive, what interviewers evaluate, what to expect, and how to prepare. TikTok tends to emphasize applied statistics tied to product metrics: A/B testing and experiment design, regression and causal reasoning, probability and sampling, variance and power, and practical diagnostics for data quality. Interviewers look for rigorous problem framing, correct use of statistical tools, clarity about assumptions, sensible tradeoffs, and the ability to translate numerical findings into product decisions. Coding fluency in SQL and Python for data wrangling is often assessed alongside statistical reasoning. For interview preparation, concentrate on core concepts (hypothesis testing, confidence intervals, power calculations, bias vs. variance, and common pitfalls), plus applied practice: design an experiment, diagnose a metric drop, and explain results to non‑technical stakeholders. Practice writing and speaking through short problem walkthroughs, rehearse common metric definitions (DAU, retention, CTR), and time-box technical drills in SQL/pandas. Mock interviews that combine stats with product sense and clear storytelling will best mirror what TikTok typically evaluates.

"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."

"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."

"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."

"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."

"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."

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

"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."

"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."
"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."
Optimize threshold using confusion matrix and costs
Calibrated Classifier on an Imbalanced Dataset (1% positives) You have a perfectly calibrated binary classifier evaluated on 10,000 held-out examples....
Interpret and validate regression with interactions
Modeling 7-day Retention with LPM and Logistic Regression Context You have user-level data with a binary outcome retained_7d (1 if the user is active ...
Derive L1 vs L2 effects with correlation
Multicollinearity: Ridge vs LASSO vs Elastic Net Setup - Two standardized predictors x1 and x2 with corr(x1, x2) = 0.99. - X'X = [[100, 99], [99, 100]...
Control confounding in observational ad lift
Estimating the ATE of Ad Exposure on Conversions (Observational Setup) You cannot randomize ad exposure. Users differ in age, education, income, and o...
Explain Type I/II errors vs precision/recall
Questions 1. Define Type I error and Type II error in hypothesis testing, and map them to false positives and false negatives. 2. Explain how Type I/I...
Compute cluster-aware significance and sequential corrections
Cluster-Randomized Tipping UI Experiment: Power, Sequential Testing, and Multiplicity Context: A creator-level (cluster) randomized experiment evaluat...
Model overdispersed counts; estimate treatment lift
Weekly posts per creator are overdispersed and zero‑inflated. In a creator‑level randomized test of a nudge: - Control: n_c=40,000 creators, total pos...
Decide if subgroup increases imply overall increase
TikTok Time: Subgroup Increases vs Overall Average (Simpson's Paradox) You are analyzing average daily time spent on TikTok by gender (male, female) a...
Differentiate LDA and QDA; compute boundary
Binary Gaussian Classification: LDA vs QDA You are modeling a 2D binary Gaussian classifier with features (x, y): - Class 0: mean μ0 = (0, 0), covaria...
Calculate Expected Draws for X > 0.8 in Uniform(0,1)
Scenario Quick probability check during a first-round screen to gauge statistical intuition. Question Let X ~ Uniform(0, 1). You draw independent samp...
Act when A/B result is not significant
A/B Test Planning and Decision-Making for a 60s Video Change Context: You are evaluating a product change with completion rate as the primary metric. ...
Use DiD for staggered treatment adoption
Staggered DiD for a Weekly RPU Rollout (50 Regions, 2025-06-01 to 2025-08-15) Context and assumptions: - You have panel data at the region-week level ...
Apply PSM rigorously for observational A/B analysis
Task: Estimate ATT on 7-Day Retention Using Propensity Score Matching (PSM) Context You are given observational, user-level product data where users s...
Test Billboard Campaign Conversion Rate Exceeds 60%
One-Proportion Test for a Conversion Rate Context You ran a billboard campaign and measured conversions on a sample of N = 100 users. The observed con...