I started with randomization and sample size, which felt safe, but they kept pushing.
Start by framing the experiment design around a clear hypothesis and success metrics, then walk through the lifecycle: randomization, sample size, instrumentation, analysis, and interpretation. Emphasize how each design choice mitigates specific pitfalls like selection bias, peeking, and confounding. Conclude with how you'd validate results and communicate uncertainty.
Pro tip: Show that you think about pitfalls before they happen—e.g., pre-registering your analysis plan and using guardrail metrics—rather than just listing biases after the fact. Mention that you'd simulate or backtest the experiment design to catch issues early.
Articulate a clear, falsifiable hypothesis and choose primary, secondary, and guardrail metrics that directly tie to the product goal. Ensure metrics are sensitive enough to detect meaningful changes.
Use proper randomization (e.g., user-level, cluster) to avoid selection bias and ensure comparable groups. Calculate required sample size and duration upfront to avoid underpowered tests.
Pre-register the analysis plan, including stopping rules, to prevent p-hacking and peeking. Set up automated monitoring for data quality and guardrail metrics to catch issues early.
Apply statistical tests that match the design (e.g., t-test, CUPED, sequential testing) and check assumptions. Control for multiple comparisons and segment analyses to avoid false positives.
Consider practical significance, confidence intervals, and potential confounders. Validate findings with holdout groups, replication, or qualitative research before making decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.