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.
HardTechnical
68 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).
MediumTechnical
66 practiced
You're building an uplift model from observational marketing data where treatment assignment was not randomized. Describe at least three statistical approaches to correct selection bias (propensity score weighting/matching, instrumental variables, regression discontinuity) and outline an evaluation strategy (diagnostics, uplift validation on holdout, uplift calibration) to gauge reliability before deployment.
MediumTechnical
66 practiced
An experiment shows covariate imbalance: distribution of age and device type differs between treatment and control despite randomization. Describe a step-by-step plan: diagnostics to confirm imbalance, root-cause checks (assignment logs), statistical fixes (blocking, stratification, regression adjustment, inverse-propensity weighting), and when you would rerun the experiment versus adjust analytically.
MediumTechnical
122 practiced
You're observing a substantial drop in retention after exposing users who had the highest engagement last month to a new feature. The product manager thinks the feature caused the drop. Outline an investigation plan to determine whether observed decline is due to regression to the mean, a true treatment effect, or data/segmentation issues. State which analyses and visualizations you'd run and what data you'd request.
HardTechnical
91 practiced
You find that treatment increases conversion within every major demographic segment but the aggregate effect is negative (Simpson's paradox). Propose a statistical analysis pipeline to detect, explain, and report this: include model choices (interaction terms vs hierarchical Bayesian), visualization strategy, pre-specification guidelines, and decision rules so executives are not misled by aggregation.
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