Pretty basic definition question but I fumbled the follow-up a bit.
Start with a clear, concise definition of A/B testing as a randomized controlled experiment comparing two versions of a product change. Then, walk through a structured example of how you would design, execute, and analyze an A/B test, emphasizing statistical rigor and business impact. Finally, connect it to Amazon's culture of experimentation and customer obsession.
Pro tip: Highlight the importance of defining a single primary metric and guardrail metrics upfront, and mention how you would handle common pitfalls like peeking or multiple comparisons. This shows you understand both the statistics and the practical challenges of experimentation at scale.
Clearly state what you are testing and why, linking it to a business objective or customer problem. Specify the null and alternative hypotheses.
Determine the target population, sample size, randomization unit, and success metrics (primary and guardrail). Ensure statistical power and avoid common design flaws.
Execute the test, ensuring proper randomization and monitoring for data quality issues. Avoid peeking at results prematurely to prevent false positives.
Use statistical tests to determine if the difference is significant, and assess practical significance. Consider segment analysis and guardrail metrics before deciding to ship, iterate, or abandon.
Share findings with stakeholders, document learnings, and apply insights to future experiments. Emphasize continuous improvement and a culture of experimentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.