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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.

HardSystem Design
0 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.
MediumTechnical
0 practiced
In SQL, write a query or pseudocode to compute a CUPED-adjusted mean conversion for treatment and control groups given a table events(user_id, treatment_group, pre_period_conversion, post_period_conversion). Show the steps to compute the covariance, theta, adjusted metric, and final group means.
HardTechnical
0 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
0 practiced
Describe how you'd implement group-sequential testing with alpha spending in production: ingestion cadence, interim-analysis triggers, API endpoints for checking stopping rules, logging of alpha spent, and handling of delayed-conversion metrics or missing data.
MediumTechnical
0 practiced
Describe sequential analysis approaches for A/B tests: group sequential testing, alpha spending, and continuous monitoring. Compare Pocock and O'Brien-Fleming boundaries and explain the pros/cons for product experiments that involve frequent interim looks.

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