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
0 practiced
You suspect heterogeneous treatment effects across user segments (region, device, prior activity). Describe a practical workflow to detect and validate heterogeneity while avoiding false positives: data partitioning, pre-specification, multiple-testing correction, and use of uplift models or causal trees. Include validation steps.
HardTechnical
0 practiced
Implement an alpha-spending schedule calculator in Python that outputs critical p-value thresholds for interim looks under Pocock and O'Brien-Fleming boundaries given a planned number of looks. The function should return thresholds per look and explain how each boundary behaves (early vs late conservatism).
EasyTechnical
0 practiced
You're asked to test whether a redesigned onboarding flow increases 7-day user retention relative to the current flow. Formulate the null and alternative hypotheses, choose a primary metric and one or two guardrail metrics, define a clear success criterion (including directionality), and explain why pre-specifying the metric and analysis plan matters. Assume a binary user-level retention metric measured at day 7.
MediumTechnical
0 practiced
Implement the CUPED variance reduction technique in Python: given a pre-experiment baseline covariate vector x and observed outcome y during the experiment for n users, compute the CUPED-adjusted outcome y_adj and show how to perform a t-test on the adjusted outcomes. Describe assumptions behind CUPED.
MediumSystem Design
0 practiced
Design an end-to-end experimentation platform that supports feature flags, randomization, event ingestion, metric computation, anomaly detection, and reporting. Include API contracts, data storage choices for event-level and aggregated metrics, how you'd ensure reproducibility of experiments, and how the platform handles ramping and rollbacks for thousands of concurrent experiments.
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