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.
EasyTechnical
0 practiced
In non-technical language, explain statistical power and why sample size matters for experiments. How would you explain to a PM peer why a 'small test' might not be conclusive, and what practical rules-of-thumb you would use when estimating experiment duration given weekly traffic and baseline conversion rate?
EasyTechnical
0 practiced
Describe ethical considerations when running product experiments that affect user trust, pricing, or privacy (for example: tests that change fees, manipulate perceived scarcity, or personalize prices). How would you balance learning goals with user rights, brand reputation, and regulatory constraints?
EasyTechnical
0 practiced
An experiment shows p=0.04 for the primary conversion metric but the critical guardrail metric (e.g., 7-day retention) has p=0.07 and a downward trend. Describe how you would evaluate these results, which additional checks and analyses you'd run, and a decision framework to recommend rollout, iterate, or rollback.
EasyTechnical
0 practiced
Explain what 'peeking' (optional stopping) is in A/B testing, why repeated interim looks inflate Type I error, and describe one practical strategy a product team can adopt to avoid peeking while still supporting business needs for early signals.
MediumTechnical
0 practiced
What is uplift modeling and when is it useful for product experiments (for example, targeting promotions or re-engagement)? Describe required inputs, common modeling approaches (two-model, meta-learners), evaluation metrics for uplift, and one operational use case where uplift targeting beats naive targeting.
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