Structure your answer around the experiment lifecycle: design (metrics, randomization, sample size), execution (monitoring, ramp, logging), and decision (stop/ship, Simpson's paradox). Emphasize how network effects and multi-tenancy complicate standard A/B testing and propose concrete solutions like cluster randomization, variance reduction, and difference-in-differences. Conclude with robust decision criteria that account for heterogeneity across tenants and roles.
Pro tip: Proactively address interference by proposing a cluster-randomized design with org as the unit, and pre-specify how you'll detect and resolve Simpson's paradox using tenant- and role-level subgroup analyses.
Choose primary (e.g., file engagement rate), secondary (e.g., click-through, session time), and guardrail (e.g., latency, error rate) metrics. Select randomization unit (org or user cluster) to handle interference.
Estimate sample size using cluster randomization with ICC assumptions; apply variance reduction techniques like CUPED or stratification by org size/role.
Set up sequential monitoring with alpha spending, control for novelty/carryover via washout periods, and design a gradual ramp schedule (e.g., 1% → 5% → 50%).
Log exposures, user interactions, and cross-user influences; attribute outcomes correctly by tracking org-level and user-level events.
Pre-register decision rules based on primary metric lift and guardrails; analyze subgroups (tenant, role) to detect and resolve Simpson's paradox before shipping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.