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.
HardTechnical
76 practiced
Some product changes have delayed effects (for example retention improvements visible after 30 days). How would you design an experiment and analysis plan to detect delayed effects while minimizing user exposure risk? Discuss cohort alignment, pre-specifying time windows and outcomes, methods to estimate cumulative effects over time, and how to avoid premature stopping.
HardTechnical
66 practiced
Given a daily aggregated table daily_results(date, bucket, users, conversions) for an experiment, write a Postgres SQL query that computes cumulative conversion rates per day for control and treatment, the cumulative absolute lift, the z-test p-value for the cumulative difference per day, and flags the first date where p-value < 0.05. Note: this replicates naive sequential peeking; explain its pitfalls in the comments.
MediumTechnical
60 practiced
You are validating a major product change that requires substantial engineering work. Describe a staged validation plan that mixes qualitative methods (user interviews, usability testing), small pilots, and controlled A/B experiments before full rollout. For each stage, list key success criteria, instrumentation needs, and how BI would measure and report outcomes.
MediumTechnical
72 practiced
Explain why 'peeking' at experiment results (checking frequently and stopping when p<0.05) inflates the false positive rate. Describe at least two statistical methods that allow interim looks without inflating Type I error (for example, alpha spending functions or group sequential methods) and discuss trade-offs for adopting them.
HardTechnical
65 practiced
You discover that 10% of events from a key data source are missing for a subset of users due to an SDK bug that affected only treatment clients. Design a remediation plan: describe how you would assess impact of the missing data on experiment inference, options for excluding or correcting affected users, sensitivity analyses you would run, and how you would communicate the issue and recommended next steps to stakeholders.
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