Type I Error

A Type I error is a false positive: rejecting the null hypothesis when it is actually true — declaring an effect that isn’t real. Its long-run rate equals the significance level α, so testing at 0.05 means about 5% of truly-null situations will trip a false alarm.

Think of a smoke detector going off when there’s no fire. Type I errors are why running many tests is dangerous: at α = 0.05, testing 20 dead-ends gives you roughly a 64% chance of at least one false “discovery,” which is the heart of the multiple-comparisons problem.