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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
58 practiced
Design an experiment and analysis plan to estimate heterogeneous treatment effects of a recommendation algorithm separately for mobile and desktop users. Include pre-specification for subgroup hypotheses, sample size allocation per subgroup, and statistical methods (interaction models, CATE estimation) you would use.
MediumTechnical
64 practiced
You observe an experiment where click-through rate increased significantly but purchase rate did not change. Outline diagnostic steps and follow-up experiments to determine if clicks are low-quality or whether there is a downstream funnel blockage. Include data slices, session-level analyses, and potential quick interventions to test hypotheses.
MediumTechnical
62 practiced
Discuss practical methods for post-hoc covariate adjustment when you observe imbalance despite randomization: ANCOVA, inverse probability weighting, and reweighting. Explain assumptions required for each, and when post-hoc adjustment is acceptable versus when you should invalidate and rerun the experiment.
EasyTechnical
62 practiced
Explain interference and network effects in experiments and why the Stable Unit Treatment Value Assumption (SUTVA) can fail. Provide a concrete example such as a referral program or social feed where one user's treatment assignment affects others, and explain qualitatively how this biases naive A/B estimates.
EasyTechnical
80 practiced
Define heterogeneous treatment effects (HTE) and give examples of methods to estimate them: subgroup analysis, CATE via meta-learners (T-learner, S-learner, X-learner), and uplift modeling. Discuss the p-hacking risk when searching for subgroups and how to reduce it.

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