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
Explain what a p-value is and how it differs from a confidence interval and an effect size. A product manager asks 'Is this result significant and important?': how would you communicate significance, uncertainty, and practical importance in plain language?
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.
EasyTechnical
0 practiced
List the key steps you would take to ensure experiment instrumentation is correct for a new metric computed from an event stream: include user identification, deduplication, event windows, time zones, sampling, data freshness, and validation checks. Provide one example of a common instrumentation bug and how you'd detect it.
MediumTechnical
0 practiced
Design a sample-size calculation for a continuous metric (e.g., daily time-on-site per user) where standard deviation is large relative to the mean. Describe how the metric's variance influences sample size, show the formula for detecting a small standardized effect (Cohen's d = 0.2) with 80% power and alpha=0.05, and explain practical implications.
MediumTechnical
0 practiced
Design a pilot study to validate a new payment method in three markets before broader rollout. Define objectives, sample selection strategy, ramp plan, primary and guardrail metrics (including fraud and chargeback), how to detect and act on adverse signals, and stopping criteria for each market.
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