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Experimentation Methodology and Rigor Questions

Focuses on rigorous experimental methodology and advanced testing approaches needed to produce reliable, actionable results. Topics include statistical power and minimum detectable effect trade offs, multiple hypothesis correction, sequential and interim analysis, variance reduction techniques, heterogenous treatment effects, interference and network effects, bias in online experiments, two stage or multi component testing, multivariate designs, experiment velocity versus validity trade offs, and methods to measure business impact beyond proximal metrics. Senior level discussion includes designing frameworks and practices to ensure methodological rigor across teams and examples of how to balance rapid iteration with safeguards to avoid false positives.

EasyTechnical
60 practiced
Explain why multiple hypothesis correction is necessary when running many experiments or evaluating multiple metrics. Compare Bonferroni correction and Benjamini-Hochberg (FDR) in terms of conservativeness and impact on power, and give situations where each is appropriate for a BI team.
HardTechnical
63 practiced
Design a pipeline to develop, validate, and deploy uplift models (treatment effect models) that personalize treatment assignment. Cover data requirements, feature engineering, model choices (uplift trees, double-robust estimators), offline evaluation metrics (Qini, AUUC), A/B validation strategy, and post-deployment monitoring.
HardSystem Design
121 practiced
Propose a scalable implementation to compute permutation-based p-values for multiple ratio metrics in near-real-time for an experimentation dashboard. Discuss sampling strategies, parallelization, approximation techniques (Monte Carlo permutations), caching, and how to present uncertainty and p-value resolution to end users.
HardTechnical
63 practiced
You have experiment data: user_id, treatment (0/1), outcome, and friend_list_size. Exploratory plots show users with many friends have different treatment effects. Describe an analysis plan to detect and estimate spillover effects using exposure mapping or regression with network features, and outline a significance testing approach under interference.
MediumTechnical
119 practiced
You observe a significant average treatment effect. Describe a rigorous process to search for and validate heterogeneous treatment effects (HTE) across user segments while controlling false positives and avoiding data dredging and overfitting.

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