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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
0 practiced
A business metric has high variance and long-tailed distribution. Propose a concrete variance reduction strategy that includes data transformations, CUPED or stratification, and instrumentation changes. Describe implementation steps, expected trade-offs, and how you would validate the variance reduction in historical data.
EasyTechnical
0 practiced
Define the Minimum Detectable Effect (MDE) and explain how it relates to statistical power, significance level (alpha), and required sample size for online A/B tests. Use a brief numeric example: baseline conversion 5%, power 80%, alpha 0.05, and discuss qualitatively how required sample size changes if MDE is 0.5% versus 2%. Mention assumptions behind normal approximation for binary outcomes.
MediumSystem Design
0 practiced
Design an analysis plan for applying multi-armed bandits to optimize homepage variants. Discuss the statistical guarantees you can and cannot get from bandit allocation, how to measure true causal effects under adaptive allocation, and practical steps to get unbiased estimates for final decision making.
MediumTechnical
0 practiced
Design and implement (pseudocode acceptable) a method to perform bootstrap-based confidence intervals for treatment effect estimate when data exhibits temporal dependence and seasonality. Discuss why naive bootstrap may fail and propose alternatives like block bootstrap or de-seasonalization before resampling.
EasyTechnical
0 practiced
Describe a checklist of checks you would run to detect bias or instrumentation issues before trusting an experiment result. Include at least five concrete checks such as assignment balance, event firing rates, pre-period metric drift, and user-level duplicate assignment detection.

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