This tripped me up more than it should have.
Start by explaining that random assignment is foundational for valid causal inference, then outline a multi-pronged verification approach: statistical tests on pre-experiment covariates, checks on assignment mechanism, and validation of bucket sizes. Emphasize that you would perform these checks before analyzing the experiment results to ensure the test is trustworthy.
Pro tip: At Amazon, where decisions are data-driven, demonstrating that you proactively validate randomization builds trust in your analysis. Mention that you would also check for sample ratio mismatch (SRM) as a standard practice, as it's a common pitfall that can invalidate experiments.
Verify that the observed split between control and treatment matches the intended ratio (e.g., 50/50) using a chi-square goodness-of-fit test. A significant deviation indicates potential assignment bias or data pipeline issues.
For key metrics and user attributes measured before the experiment, compare distributions across buckets using t-tests, chi-square tests, or Kolmogorov-Smirnov tests. No significant differences suggest random assignment.
Review the randomization algorithm (e.g., hashing user IDs) to ensure it's deterministic, uniform, and independent of user characteristics. Check for any bugs or biases in the implementation.
Plot the proportion of users assigned to each bucket over time and across key segments (e.g., geography, device) to detect any drift or non-random patterns.
If feasible, run an A/A test where both groups receive the same experience. Any significant difference in metrics indicates a problem with randomization or the experimentation platform.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.