I started with user assignment and bucketing logic, which felt right, but I think I spent too long there and barely touched metrics infrastructure or statistical significance.
Start by clarifying the product context and experiment goals, then walk through the end-to-end system: assignment, data collection, analysis, and decision-making. Emphasize statistical rigor, scalability, and how you'd balance speed with trust in results.
Pro tip: Show you understand the trade-offs between centralized vs. decentralized experimentation platforms, and mention how you'd handle network effects or interference—common at Google scale.
Clarify what you're testing, the primary success metric (e.g., click-through rate, revenue), and guardrail metrics to ensure no harm. Align with stakeholders on the hypothesis and expected impact.
Explain how users are randomly assigned to variants (e.g., via hashing user IDs) to ensure unbiased groups. Discuss stratification, sample size calculation, and handling of edge cases like new users.
Describe logging events, ensuring data quality, and tracking exposure to the experiment. Mention the need for a consistent event schema and real-time monitoring for anomalies.
Outline statistical methods (e.g., t-test, sequential testing) to determine significance, and how to interpret results considering practical significance. Discuss how to handle multiple comparisons and peeking.
Talk about automating the pipeline, building a self-serve platform, and creating a culture of experimentation. Include how to learn from failures and iterate quickly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.