I started talking about the retention metric too fast and the interviewer kind of steered me back with 'but how do you know the groups are even comparable?' which, fair point.
Start by validating the experiment's integrity: check if the traffic split was random and balanced across key dimensions, and if the retention metric was measured consistently. Then, isolate the effect of allocation by comparing pre-experiment characteristics and running falsification tests, before attributing the drop to the button color.
Pro tip: Always check for Sample Ratio Mismatch (SRM) first—it's a common and often overlooked issue that can invalidate results. If SRM exists, the experiment is compromised, and you should not trust any metric differences.
Verify that the actual traffic split matches the intended ratio (e.g., 50/50) using a chi-square test. If SRM is present, the randomization or logging may be flawed, and the retention drop could be due to biased allocation.
Compare pre-experiment covariates (e.g., user demographics, past behavior) between control and treatment groups. If groups differ significantly, the allocation may have introduced confounding, not the button color.
Ensure retention is defined consistently and measured correctly for both groups. Check for data loss, logging errors, or differences in how events are captured that could artificially lower retention in one group.
Test for effects on metrics that should not be impacted by button color (e.g., pre-experiment retention). If these show differences, it suggests allocation bias. Also, analyze subgroups to see if the drop is concentrated in specific segments.
If allocation issues are found, recommend fixing the experiment and re-running. If not, and the drop is robust, conclude the color change likely caused harm, but consider practical significance and potential novelty effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.