Type I and type II errors in statistics

Saturday, November 15th, 2014

Quoting from Wikipedia

In statistical hypothesis testing, type I and type II errors are incorrect rejection of a true null hypothesis or failure to reject a false null hypothesis, respectively. More simply stated, a type I error is detecting an effect that is not present, while a type II error is failing to detect an effect that is present. The terms “type I error” and “type II error” are often used interchangeably with the general notion of false positives and false negatives in binary classification, such as medical testing, but narrowly speaking refer specifically to statistical hypothesis testing in the Neyman–Pearson framework, as discussed in this article.


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