Type 1 and Type 2 error - SoojungHong/StatisticalMind GitHub Wiki

Type 1 Error

In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding). It means null hypothesis is true, but it reject it. e.g.) H0 : Earth is the center of the universe. This is wrong, but it reject, so False Positive error.

Type I error, also known as a “false positive” : the error of rejecting a null hypothesis when it is actually true.

외우는 방법 타입 1 에러 : 가정이 맞지만 (positive) 틀렸다고 (false) 하는 것 - false positive 타입 2 에러 : 가정이 틀리지만 (negative) 맞다고 하는 것 - false negative

Type 2 Error

type II error is failing to reject a false null hypothesis (also known as a "false negative" finding).[1] More simply stated, a type I error is to falsely infer the existence of something that is not there, while a type II error is to falsely infer the absence of something that is. (e.g.) H0 : Newton was hit by an apple (he wasn’t). The hypothesis is wrong, it should be rejected. But it accept. so false negative.

Type II error, also known as a "false negative": the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature.