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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
You need to test three independent features simultaneously: UI change (A), recommendation ranking (B), and incentives (C). Design a factorial experiment: specify the allocation, how to test for interactions, and how to estimate marginal effects. What sample size considerations are unique to factorial designs?
MediumTechnical
0 practiced
Write a Python function that calculates required sample size per variant for a two-sided A/B test given baseline conversion p0, desired relative lift (as fraction), alpha, and power. Explain the formulas and assumptions used. Example input: p0=0.05, relative_lift=0.10, alpha=0.05, power=0.8.
HardSystem Design
0 practiced
Marketplaces often have cross-side network effects (buyers and sellers). Design an experiment to measure both direct effects on treated sellers and indirect effects on buyers and untreated sellers. Specify randomization unit, metrics, analysis strategy, and how to account for interference.
HardTechnical
0 practiced
Describe statistical methods to detect interference using exposure logs and permutation testing. Propose a test statistic, explain how to permute assignments under interference, and describe limitations of the approach.
EasyTechnical
0 practiced
Briefly explain the difference between an A/B test and a multi-armed bandit approach. For which practical business situations would you prefer bandits over fixed-horizon A/B tests, and why?

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