Experimentation and Product Validation Questions
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
MediumTechnical
119 practiced
You notice a sample ratio mismatch (observed treatment fraction 42% vs expected 50%). Provide a prioritized debugging checklist: quick checks to run immediately, deeper instrumentation and code checks, server/client bucketing audits, and how to assess whether SRM materially biases your ATE estimate.
EasyTechnical
69 practiced
For an experiment that changes the in-session search ranking algorithm on a mobile app, explain how you would choose the randomization unit (user, device, session). Discuss the advantages and disadvantages of each option, likely sources of bias (e.g., contamination, cross-device users), and the expected impact on statistical power and interpretability.
HardTechnical
102 practiced
You plan to deploy a new backend that reduces cost but may slightly increase latency. Formulate a non-inferiority test for user engagement to demonstrate the cheaper backend is acceptable. Specify null and alternative hypotheses, how to choose a non-inferiority margin, sample size calculation considerations, and the interpretation of results.
MediumTechnical
53 practiced
Compare multi-armed bandits and standard randomized A/B tests across exploration-exploitation trade-offs, regret, sample efficiency, inferential validity, and operational complexity. Provide one concrete product scenario where Thompson sampling or an explore/exploit bandit is preferable and one scenario where a classical randomized A/B test is the better choice.
MediumTechnical
53 practiced
Describe why optional stopping (peeking) inflates false positive rates in classical hypothesis tests. Present two practical sequential/continuous-monitoring approaches appropriate for product teams (for example, alpha-spending boundaries and Bayesian monitoring). Discuss operational trade-offs and how you'd implement one approach in an experimentation platform.
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