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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.

MediumTechnical
61 practiced
Explain the CUPED (Controlled-experiment Using Pre-Experiment Data) variance reduction technique. Derive the optimal control variate coefficient theta = Cov(X,Y) / Var(X) given pre-experiment covariate X and outcome Y, and describe how to implement CUPED in an A/B testing pipeline. Discuss failure modes when X is affected by treatment.
HardTechnical
67 practiced
Design an organization-wide experimentation governance framework for a company scaling from a few experiments a month to thousands per quarter. Cover experiment registration and pre-specification, metric and guardrail registries, peer-review and approval processes, platform enforcement points (e.g., stopping rules), auditing tools, and training programs. Describe a phased rollout.
EasyTechnical
69 practiced
Explain multiple hypothesis correction approaches used in large-scale experimentation: Bonferroni correction, Holm-Bonferroni, and Benjamini-Hochberg (FDR). When would you prefer family-wise error rate (FWER) control versus false discovery rate (FDR) control in product experiments, and what operational practices complement these corrections?
MediumTechnical
62 practiced
Design an experiment to measure the long-term retention effect of a new onboarding flow, where the primary outcome is 90-day active users. Specify sample-size logic, experiment duration, intermediate (surrogate) metrics, handling of delayed conversions and censoring, and methods to accelerate learning without biasing long-term estimates.
HardTechnical
102 practiced
SUTVA is violated in your social network experiment (partial interference). Propose an estimator and identification strategy for average direct and spillover effects when interference is confined within clusters. Include assumptions, how to map exposures, weighting/HT estimators, and variance estimation that accounts for clustering.

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