In hypothesis testing, rejecting a null hypothesis that is actually true is called what?

A Type I (alpha) error is concluding there is an effect when none exists (false positive). A Type II (beta) error is missing a true effect (false negative).Other options: Power is the ability to detect a true effect (1 - beta); confounding is a third-variable distortion.Key exam takeaway: Type I = false positive (reject true null); Type II = false negative (fail to reject false null).