Type II error:
A Type II error (sometimes called a Type 2 error) is the failure to reject a false null hypothesis. The probability of a type II error is denoted by the beta symbol β. In order to understand your probability of making a Type 2 error, you’ll want to understand what a null hypothesis is and what an alternate hypothesis is (click the links for plain English explanations of these two types of hypothesis).Let’s say you’re an urban legend researcher and you want to research if people believe in urban legends like:
- Newton was hit by an apple (he wasn’t)
- Walt Disney drew Mickey mouse (he didn’t — Ub Werks did)
- Marie Antoinette said “Let them eat cake” (she didn’t).
The accepted fact is, most people probably believe in urban legends* . So, your null hypothesis is:
H0Most people do believe in urban legends.
But let’s say that null hypothesis is completely wrong. It might have been true ten years ago, but with the advent of the Smartphone — we have Snopes.com and Google.com at our fingertips. Still, your job as a researcher is to try and disprove the null hypothesis. So you come up with an alternate hypothesis:
H0Most people DO NOT believe in urban legends.
You conduct your research by polling local residents at a retirement community and to your surprise you find out that most people do believe in urban legends. The problem is, you didn’t account for the fact that your sampling method introduced some bias…retired folks are less likely to have access to tools like Smartphones than the general population. So you incorrectly fail to reject the false null hypothesis that most people do believe in urban legends (in other words, most people do not, and you failed to prove that). You’ve committed an egregious Type II error, the penalty for which is banishment from the scientific community.
*I used this simple statement as an example of Type I and Type II errors. I haven’t actually researched this statement, so as well as committing numerous errors myself, I’m probably also guilty of sloppy science!
