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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.

EasyTechnical
65 practiced
What is interference (spillover) in experiments and why does it violate the Stable Unit Treatment Value Assumption (SUTVA)? Give two real-world product examples where interference is likely and explain the implications for analysis.
MediumTechnical
118 practiced
You are planning an experiment to increase conversion. Baseline conversion = 2%, desired power = 90%, alpha = 0.05 two-sided, target MDE = 10% relative uplift. Calculate the required sample size per arm and explain assumptions. Show the formula and approximate final number.
MediumTechnical
69 practiced
You're running a 2x2 experiment where two features A and B are tested both independently and together. How would you estimate and test whether the interaction A*B is significant? Provide the regression specification you would use and explain how to interpret the interaction coefficient.
EasyTechnical
65 practiced
Calculate the minimum detectable effect (MDE) for a binary conversion metric given: baseline conversion = 5%, sample size per arm = 10,000, two-sided alpha = 0.05, desired power = 80%. Show the formula, intermediate steps, and approximate MDE in absolute percentage points.
EasyTechnical
62 practiced
Describe variance reduction techniques commonly used in online experiments: stratification/blocking, CUPED (control variates), and regression adjustment. For each technique explain the intuition for how it reduces variance and one practical caveat when applying it to product telemetry.

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