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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
60 practiced
What is an alpha-spending function and how does it differ from fixed group-sequential boundaries (e.g., Pocock, O'Brien–Fleming)? Provide a short example of choosing an alpha-spending approach suitable for business experiments that require frequent interim checks early in the experiment lifecycle.
EasyBehavioral
69 practiced
Behavioral: Tell me about a time when you advocated for stricter experimental rigor (e.g., pre-registration, holdouts, correction for multiple tests) on a team that preferred rapid iteration. What was the situation, what actions did you take, and what were the outcomes?
MediumTechnical
76 practiced
You detect time-varying treatment effects in your experiment: the treatment effect is positive weekdays but negative weekends. How would you investigate whether this pattern is real, an artifact of traffic composition, or due to time-based confounding? Propose statistical tests and robustness checks.
EasyTechnical
60 practiced
You run an A/B test and observe a sample ratio mismatch (SRM): traffic was split 60/40 instead of 50/50 to your experiment variants. List possible causes of SRM in an online experimentation system and outline a troubleshooting checklist (instrumentation, sampling, routing, bots, API changes).
MediumTechnical
81 practiced
Explain how cluster-randomized (e.g., geo or user-group) experiments affect variance and power compared to individual randomization. Provide the formula for the design effect using intra-cluster correlation (ICC) and show how it scales required sample size.

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