Type I and Type II Errors
Also called: false positive, false negative, type 1 error, type 2 error
A Type I error rejects a true null hypothesis — a false positive. A Type II error fails to reject a false one — a false negative. The two trade off against each other: demanding stronger evidence cuts false positives and raises false negatives. Which matters more is a business decision, not a statistical one, since a missed fraud and a wrongly frozen account have very different costs.
Want more than a definition? Learn it in Stat Dojo →