Feature Success Measurement Questions
Focuses on measuring the impact of a single feature or product change. Key skills include defining a primary success metric, selecting secondary and guardrail metrics to detect negative side effects, planning measurement windows that account for ramp up and stabilization, segmenting users to detect differential impacts, designing experiments or observational analyses, and creating dashboards and reports for monitoring. Also covers rollout strategies, conversion and funnel metrics related to the feature, and criteria for declaring success or rollback.
MediumTechnical
0 practiced
When using sequential monitoring for experiments, how would you adjust sample size and stopping rules compared to fixed-horizon testing? Describe alpha spending functions or group sequential methods and practical guidelines for product teams who want early stopping capability.
MediumTechnical
0 practiced
A traffic assignment bug caused treatment to have more high-value users. Describe statistical approaches to control for this imbalance in the analysis, including covariate adjustment, propensity weighting, and sensitivity checks. Explain pros and cons of each approach when assignment is non-random.
MediumTechnical
0 practiced
You need to compute the minimum sample size for an A/B test that detects a lift from 5.0% to 7.0% conversion with 80% power and alpha 0.05 two-sided. Provide the sample size formula, implement or outline a short Python snippet to compute it, and list assumptions behind the calculation.
MediumTechnical
0 practiced
Design a metric and instrumentation plan to measure 'checkout friction' for a mobile checkout flow. Describe the event taxonomy you would collect, the derived step metrics, how to calculate drop-off at each step, and how to attribute improvements to a specific UI change.
MediumTechnical
0 practiced
An A/B test shows no overall lift but results split by tenure show positive lift for new users and negative for power users. Describe statistical checks and business analyses to validate this heterogeneous effect, how to avoid p-hacking, and how you would recommend proceeding with rollout.
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