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CRO Glossary

Peeking Problem

The inflated false-positive risk that comes from repeatedly checking test results before the planned sample size is reached.

The peeking problem happens when a team monitors an experiment's results continuously and stops the test as soon as it crosses a significance threshold, rather than waiting until a pre-determined sample size or duration is reached. Each peek is effectively another chance for random noise to look like a real effect, and with enough peeks, the true false-positive rate can climb from a nominal 5% to 20%, 30%, or higher.

This matters because it's an extremely common and intuitive mistake — dashboards update in real time, and it's natural to check in daily and call a winner the moment the numbers look good. The problem isn't checking the dashboard itself; it's making a stop/ship decision based on an interim peek rather than the pre-registered analysis plan.

The standard fixes are to either commit to a fixed sample size and analysis date decided before the test starts, or to use sequential testing methods designed explicitly to allow valid peeking. For example, a team that plans to run a test for two weeks but checks results daily should either resist stopping early, or use a sequential testing framework that adjusts thresholds to keep the overall error rate under control.

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