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
89 practiced
Define Type I and Type II errors in the context of A/B testing for conversion rate. Give a concrete numeric example (e.g., baseline conversion 5%, treatment observed 6.5% with p=0.04) and explain which error each decision (launch vs not launch) risks. Discuss the business consequences of both error types and how you would balance them when prioritizing growth experiments.
MediumTechnical
97 practiced
Compare Bayesian and frequentist approaches to A/B testing in a high-velocity growth organization. Discuss interpretability for PMs, sample size planning, handling of optional stopping, how priors can be used (and misused), and practical recommendations when one approach is preferable over the other.
MediumTechnical
68 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).
EasyTechnical
96 practiced
Explain regression to the mean and give a concrete product example (e.g., selecting top-performing users last month and observing declines after an experiment). How would you design an analysis and controls to distinguish true treatment effects from regression to the mean?
MediumTechnical
73 practiced
How would you perform power analysis for a two-sample t-test when the population standard deviation is unknown? Describe ways to estimate SD (historical data, pooled SD, pilot study), how to incorporate uncertainty about SD into sample size planning, and a simple numeric algorithm to compute required sample size for given alpha, power, and effect size.
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