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.
MediumSystem Design
0 practiced
Design an A/B test for a redesigned checkout flow. Requirements: site has 1M daily visitors, baseline conversion 3% (purchase), target detectable relative lift 5%, alpha=0.05, power=0.8. Describe metric definitions (primary and guardrails), sample size calculation (absolute MDE), randomization scheme, stopping rules, instrumentation checks, rollout plan, and how you'd measure and mitigate risk to revenue.
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.
MediumTechnical
0 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.
EasyTechnical
0 practiced
Explain Simpson's paradox with a short business example: two user segments where treatment increases conversion within each segment but aggregation across segments gives the opposite direction. Describe diagnostics to detect Simpson's paradox and practical safeguards to avoid misleading aggregated decisions.
MediumBehavioral
0 practiced
Product managers report p<0.05 on day 3 and want to stop the experiment and roll out the treatment. You're the data scientist. Draft a response that explains peeking risks, propose immediate analyses to run (e.g., SRM, funnel checks, look at effect stability over users/time), and provide a short script you would use to persuade stakeholders to follow proper stopping rules or use a sequential method.
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