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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.
EasyTechnical
81 practiced
Explain what interference and network effects are in experiments, how they violate the Stable Unit Treatment Value Assumption (SUTVA), and provide examples of partial interference. Discuss pros and cons of cluster randomization as a mitigation strategy and key design considerations.
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.
EasyTechnical
73 practiced
Define heterogeneous treatment effects (HTE). Describe practical workflows for discovering HTE in product experiments, including pre-specified subgroup analysis, interaction terms in regression, and modern methods like causal forests or meta-learners. Discuss the risks of overfitting and false positives in exploratory HTE discovery.
MediumTechnical
103 practiced
Instrumentation issues produce missing treatment assignments for 5% of users in an experiment. Propose a diagnostic plan to determine if missingness is random, and describe statistical adjustments and robustness checks (e.g., inverse-probability weighting, multiple imputation, worst-case bounds) to produce defensible treatment effect estimates.

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