Real World Experimental Challenges and Solutions Questions
Discuss practical complications in running experiments at scale: user heterogeneity, segment-specific effects, long-term vs. short-term metrics, novelty effects, network effects, and infrastructure constraints. Know techniques for variance reduction (CUPED), segmentation strategies, and how to detect and correct for data quality issues during experiments.
MediumTechnical
0 practiced
Explain sequential testing and the problem of inflating Type I error with repeated peeks. Describe practical methods for controlling error across interim looks: alpha spending, O'Brien-Fleming, Pocock, and Bayesian monitoring. Which approach would you use when product managers want continuous monitoring and why?
MediumTechnical
0 practiced
Derive the expected variance reduction from applying a single-covariate CUPED adjustment in the simplest case. Assume metric Y, covariate X with correlation rho and equal treatment/control allocation. Show relation between rho and percent variance reduction.
EasyTechnical
0 practiced
Define interference in randomized experiments and give two concrete product examples where SUTVA is violated. For each example, propose at least one experimental design or analysis approach to mitigate or estimate the interference.
HardSystem Design
0 practiced
Design a metrics pipeline to support long-term revenue as an experimental metric. Consider event ingestion, attribution windows, deduplication, delayed events, backfill, identity resolution, and computing per-user lifetime value with censoring. Describe data stores, job schedules, and how to ensure reproducibility of metric computations.
HardTechnical
0 practiced
Treatment causes higher attrition in the experiment, producing missing outcome data that is likely not missing at random. Describe statistical strategies to analyze such experiments: inverse probability weighting, survival analysis / censoring techniques, sensitivity analyses, and how you would present results and uncertainty to stakeholders.
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