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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
70 practiced
Explain 'interference' and the Stable Unit Treatment Value Assumption (SUTVA). Provide two concrete examples of interference in recommender systems or social products and the immediate experimental consequences.
HardSystem Design
81 practiced
Design an experiment registry and lineage system for reproducibility and auditability. Specify mandatory metadata (experiment id, hypothesis, primary metric, allocation, start/end times), APIs for creation and status updates, role-based access control, and retention policies. How would you integrate it with analytics and model versioning systems?
HardTechnical
60 practiced
Design a methodology for measuring the business impact of a developer-facing generative AI feature (e.g., code completion) where the target KPI is developer productivity. Propose proxy metrics, an experiment design, validation experiments, and how you'd measure long-term impact on retention and code quality.
MediumTechnical
64 practiced
Explain how you would use synthetic control or difference-in-differences (DiD) to evaluate an experiment rolled out to a subset of regions. What assumptions must hold, how would you check parallel trends, and how would you estimate uncertainty?
HardTechnical
67 practiced
Design a governance framework to reduce p-hacking and false positives across teams. Include practices such as experiment pre-registration, blinded analysis, experiment registry, code reviews, automated checks, and post-experiment audits. How would you balance these controls with team velocity?

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