Type II Error
01 Definition
Type II Error meaning: A false negative: missing a real improvement because the test failed to detect it.
A Type II error happens when a test misses a real difference and keeps the null hypothesis of no effect. It often comes from too small a sample or too short a run. The chance of avoiding this error is called statistical power, and low power means real wins can slip through unnoticed.
Also called
Why it matters
How Type II Error fits the work
Watching for Type II error stops teams from discarding good ideas that a weak, underpowered test simply could not detect.
In context
The test found no significant difference, but the sample was too small, so a genuine 3 percent gain went undetected as a Type II error.
Practical note
Small samples and short durations raise this risk. Estimate the effect you want to catch and size the test for adequate power, commonly around 80 percent, before launch.