Why does peeking at an A/B test inflate false positives, and how do sequential and always-valid tests fix it?
Checking an experiment daily and stopping the moment it hits significance can triple your false-positive rate. The signal is knowing why, and the family of methods that make continuous monitoring valid. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Checking an experiment daily and stopping the moment it hits significance can triple your false-positive rate. The signal is knowing why, and the family of methods that make continuous monitoring valid. Here is the answer.
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.
