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

129 practiced

Explain Type I (false positive) and Type II (false negative) errors in the context of an A/B test measuring conversion rate on an e-commerce checkout flow. Provide concrete business examples of the cost for each error and discuss how you would choose significance level (alpha) and power (1-beta) for a high-volume product versus a low-volume product.

MediumTechnical

92 practiced

Draft a short pre-registration template for BI experiments. What fields would you require (e.g., hypothesis, primary metric, numerator/denominator definitions, sample size, analysis plan, stopping rules, start/end dates) and how would you enforce or audit adherence to the plan in your team?

HardTechnical

86 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?

MediumTechnical

66 practiced

You run 20 experiments simultaneously and measure 10 metrics per experiment. Describe statistical strategies to control the multiple comparisons problem while still allowing exploration. Compare Bonferroni correction, Benjamini-Hochberg FDR, and hierarchical testing, and recommend a practical approach for a BI team with limited statistical resources.

HardTechnical

80 practiced

You need to measure treatment effect on a daily DAU metric that has strong weekly seasonality and autocorrelation. Describe analysis approaches such as time-series decomposition, ARIMA with intervention terms, difference-in-differences using pre-trends, and synthetic control. Outline concrete steps to implement the analysis in SQL and Python.

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