Statistical Rigor & Avoiding Common Pitfalls Questions
Demonstrate deep understanding of statistical concepts: power analysis, sample size calculation, significance levels, confidence intervals, effect sizes, Type I and II errors. Discuss common mistakes in test interpretation: peeking bias (checking results too early), multiple comparison problem, regression to the mean, selection bias, and Simpson's Paradox. Discuss how you've implemented safeguards against these pitfalls in your testing processes. Provide examples of times you've caught flawed analyses or avoided incorrect conclusions.
EasyTechnical
0 practiced
What is the multiple comparisons (multiple testing) problem? Provide a realistic growth example (e.g., testing 10 UI variants across 3 segments) and explain why naive per-test p-values inflate Type I error. Name common correction methods (Bonferroni, Benjamini-Hochberg) and when you would use each in an experimentation program.
EasyTechnical
0 practiced
What is Sample Ratio Mismatch (SRM) in randomized experiments? Describe common causes (bucketing bugs, instrumentation mismatch, eligibility filters) and provide a practical checklist (statistical test and operational steps) you would run to detect and remediate SRM before trusting experiment data.
HardTechnical
0 practiced
Construct a realistic end-to-end failure case where Simpson's paradox leads to a wrong pricing decision: define product, segmentation (e.g., new vs returning customers), show synthetic KPI numbers that create the paradox (higher conversion per-segment but lower aggregate revenue), and outline detection steps, remediation, and an executive communication plan that quantifies risk and recommended next steps (canary, segmented rollout, or further testing).
HardSystem Design
0 practiced
Design an experiment to measure the long-term (6-month) retention impact of a product feature when short-term metrics show no effect. Include randomization design, holdout group strategy (how long to keep holdouts), instrumentation to track cohort entry and censoring, metrics (survival curves, CLTV), handling of right-censoring, and sample size considerations for event-driven power.
MediumTechnical
0 practiced
Explain cluster-randomized trials and when they are necessary in growth experiments (for example, store-level promotions or household-level changes). Describe how clustering affects sample size via the design effect, how to estimate intraclass correlation (ICC), and statistical analysis methods (cluster-robust SEs, mixed-effects models).
Unlock Full Question Bank
Get access to hundreds of Statistical Rigor & Avoiding Common Pitfalls interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.