Type II Error (False Negative)

Failing to detect a real effect in a test, often because the sample was too small or the test underpowered.

By Brandon MinaCEO, BrandPilot AIUpdated November 2026

Short answer

A Type II error, or false negative, is failing to detect a real effect in a test, concluding there is no difference when one exists. It often happens because the sample was too small or the test was underpowered. Increasing sample size and running tests for their planned duration reduce the risk.