Type I error

A Type I error is a false alarm, the mistake of finding an effect that is not really there. Its long-run probability equals the significance level $\alpha$ you choose, so setting $\alpha = 0.05$ accepts a 5% chance of this error when the null hypothesis is actually true. For example, convicting an innocent defendant is a Type I error when the null hypothesis is innocence. Lowering $\alpha$ reduces Type I errors but makes Type II errors more likely, so the two trade off.