Summary Research Methods - Data Analysis I

RM - Unit 105 - Type I and Type II errors


Book: Analysing Data Using Linear Models
Chapter 2: 2.16
Chapter 2.16: Type I and type II errors
Type I error - the mistake of rejecting the null hypothesis, while it is true


Type II error - the mistake of not rejecting the null hypothesis, while it is not true


α - the relative frequency we allow ourselves to make a type I error


β - the relative frequency of making a type II error


Video: Type I error versus Type II error
Type I error is also called a false positive, type II error is also a false negative.
The goal of hypothesis testing is to reject a null hypothesis.


Example: There is this boy in the other class who you like. He likes you back but you do not ask him out.
This is a type I error, he did like you back but you assumed he didn’t; you rejected it while it was true.
Now you missed your chance.
Now you ask him out but he does not like you back. He has an older girlfriend.This is a type II
error, you assumed it was true while it was not. You wrongly invited him out. This is not as bad as a type
I error.