AI engineering glossary · Statistics inference
What is Type II error?
Type II error (also called False negative): Failing to reject a null hypothesis that is actually false, a missed real effect. Its probability is called beta, and power equals 1 - beta. It is more likely when the sample is small or the true effect is subtle.
Type II error: an example
Discarding a genuinely better checkout design because a small test could not detect its improvement.
Related terms
More Statistics inference terms
- Unbiased estimator
- Welch's t-test
- Wilcoxon signed-rank test
- Z-test
- A/B test
- Adjusted R-squared
- Alternative hypothesis
- ANOVA
Where you learn it
Type II error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.