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
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.
MediumTechnical
0 practiced
Describe selection bias in observational analyses (for example, self-selected treatment or missing-at-random problems). Provide concrete BI strategies to detect selection bias and methods (matching, propensity scores, instrumental variables, difference-in-differences) to mitigate it, including practical caveats about each approach.
HardSystem Design
0 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.
HardSystem Design
0 practiced
Design a monitoring and alerting system to detect instrumentation failures, sudden metric shifts, contamination, and negative business impact during experiments. Specify real-time checks, dashboards, automated tests, alert thresholds, escalation paths, and rollback criteria that a BI team should implement.
HardTechnical
0 practiced
You observe a positive lift at the overall product level but a negative lift for a high-value customer segment. Walk through your investigation: what data checks and diagnostics do you run, which confounders or weighting issues might explain the pattern, and how would you present reconciled findings to stakeholders?
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