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.
MediumTechnical
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Write Python code using pandas/numpy to bootstrap a 95% confidence interval for the median revenue per user given an array of user_revenue values. Explain choices for number of bootstrap samples, random seed, and interpret percentile intervals versus bias-corrected intervals.
EasyTechnical
0 practiced
You're building a Power BI dashboard that displays results of ongoing A/B tests for product managers. List the visual elements and contextual metadata you would include to avoid misinterpretation (e.g., primary metric, effect size, confidence intervals, sample size, pre-specified stopping rules, experiment status). Explain why each item matters.
EasyTechnical
0 practiced
Define Minimum Detectable Effect (MDE) and explain how it relates to sample size, baseline variance, and business decision thresholds. Provide a numeric example showing how different MDE choices change required sample size for a binary conversion metric.
MediumTechnical
0 practiced
You plan to enroll the top 5% of users by spend last month into a loyalty program experiment. Explain the risk of regression to the mean producing apparent uplift and propose experimental design or analysis adjustments to avoid attributing regression to the treatment effect.
HardTechnical
0 practiced
Design an experiment to measure uplift of a new social feature in a product where users influence each other (network interference). Discuss design options such as cluster assignment (graph clusters), seeding experiments, exposure modeling, partial population assignment, and analysis strategies to estimate direct and spillover effects.
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