AI engineering glossary · Statistics inference
What is Type I error?
Type I error (also called False positive): Rejecting a null hypothesis that is actually true, a false positive. Its long-run rate is controlled by the significance level. It is the risk you accept, in advance, of acting on an effect that is not real.
Type I error: an example
Launching a feature because a test showed a lift, when the lift was only random noise.
Related terms
More Statistics inference terms
- Type II error
- Unbiased estimator
- Welch's t-test
- Wilcoxon signed-rank test
- Z-test
- A/B test
- Adjusted R-squared
- Alternative hypothesis
Where you learn it
Type I 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.