Your A/B test shows the control and treatment groups differ before the treatment even applies. What's wrong?
A pre-existing gap between your groups means randomization or instrumentation is broken, and the entire experiment is suspect. Strong candidates spot sample-ratio mismatch immediately. Here is the full diagnosis and the disciplined response.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A pre-existing gap between your groups means randomization or instrumentation is broken, and the entire experiment is suspect. Strong candidates spot sample-ratio mismatch immediately. Here is the full diagnosis and the disciplined response.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
