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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.

HardTechnical
0 practiced
Prepare a quantitative cost-benefit framework for leadership weighing rapid decentralized feature testing against centralized experiment review that slows velocity but reduces false positives. Include expected value of information, expected rollout cost of false positives, team throughput metrics, and recommend a hybrid governance policy with thresholds for centralized review.
MediumSystem Design
0 practiced
Design an alerting and monitoring system to detect instrumentation drift, sampling bias, or experiment winner reversals after rollout. Specify which signals to track (assignment balance, event rates, demographic mix), alert thresholds, escalation paths, and automated rollback options if critical thresholds are breached.
HardTechnical
0 practiced
Design a company-wide protocol for pre-registered experiments to reduce p-hacking and increase reproducibility. Include templates for hypothesis, primary metric, sample size plan, stopping rules, and post-analysis reporting. Propose automated checks and enforcement steps, training, and metrics to measure adoption and effectiveness.
HardTechnical
0 practiced
Design an experiment to attribute multi-touch conversions in a long funnel where treatments affect both quantity and quality of traffic. Discuss causal attribution approaches, survival analysis for delayed conversions, incremental LTV estimation, and how to handle censoring and sample attrition in analysis.
MediumTechnical
0 practiced
Implement a simple sequential testing monitor in Python that, given pre-specified look times and overall alpha, computes Pocock and O'Brien-Fleming critical z thresholds for each interim look. Pseudocode or code is fine. Explain how you would integrate this monitor into a production experiment pipeline.

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