I went with a pretty textbook scenario about testing a UI change on a high-traffic surface and measuring click-through.
Choose a specific product scenario where you had a clear hypothesis about a change that could impact a measurable metric, and explain why A/B testing was the best way to validate it. Walk through the setup, execution, and how you would interpret results to make a data-driven decision.
Pro tip: Emphasize the importance of defining a clear primary metric and guardrail metrics upfront, and mention how you would handle common pitfalls like novelty effects or insufficient sample size.
Briefly describe the product, the problem or opportunity, and the proposed change. Explain why you needed to test it rather than just shipping it.
State a clear, testable hypothesis: 'If we do X, then metric Y will improve by Z% because...' This shows you understand the causal relationship you're testing.
Outline the A/B test design: control vs. variant, randomization unit, sample size calculation, duration, and success metrics (primary and guardrails).
Explain how you would analyze the data: statistical significance, confidence intervals, segment analysis, and checking for novelty or primacy effects.
Describe the decision based on results (ship, iterate, or kill) and how you would communicate findings and next steps to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.