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.
HardSystem Design
79 practiced
Propose an algorithm to detect Simpson's Paradox across many experiments and segments in a BI platform. Define statistical thresholds for flagging conflicts (aggregate sign opposite to majority of segments), how to rank severity by business impact, how to present conflicting aggregate vs segment results, and suggested next steps for product teams.
EasyTechnical
73 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
86 practiced
You run an experiment and observe p-value = 0.03 for a reported 0.2% lift in conversion. It is statistically significant but stakeholders argue the lift is negligible. Describe the analytical steps and the communication you would use to decide whether to roll out the change.
HardTechnical
90 practiced
Design an experimentation framework to run cluster-randomized trials (e.g., by geographic region or store) where users within clusters interact and interference exists. Address assignment strategy, the effect of intra-cluster correlation (ICC) on sample size, recommended analysis methods (mixed models or cluster-robust SEs), and monitoring for contamination across clusters.
MediumTechnical
84 practiced
Explain how to use covariate adjustment (ANCOVA or regression adjustment) to improve precision of A/B test estimates. Provide the basic regression formula for continuous outcomes and for binary outcomes (logistic), discuss assumptions, and name pitfalls (e.g., controlling for post-treatment variables).
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