11.2: Two Types of Errors
Before going into details about how a statistical test is constructed, it’s useful to understand the philosophy behind it. I hinted at it when pointing out the similarity between a null hypothesis test and a criminal trial, but I should now be explicit. Ideally, we would like to construct our test so that we never make any errors. Unfortunately, since the world is messy, this is never possible. Sometimes you’re just really unlucky: for instance, suppose you flip a coin 10 times in a row and it comes up heads all 10 times. That feels like very strong evidence that the coin is biased (and it is!), but of course there’s a 1 in 1024 chance that this would happen even if the coin was totally fair. In other words, in real life we always have to accept that there’s a chance that we did the wrong thing. As a consequence, the goal behind statistical hypothesis testing is not to eliminate errors, but to minimise them.
At this point, we need to be a bit more precise about what we mean by “errors”. Firstly, let’s state the obvious: it is either the case that the null hypothesis is true, or it is false; and our test will either reject the null hypothesis or retain it. 160 So, as the table below illustrates, after we run the test and make our choice, one of four things might have happened:
| retain H 0 | reject H 0 | |
|---|---|---|
| H 0 is true | correct decision | error (type I) |
| H 0 is false | error (type II) | correct decision |
As a consequence there are actually two different types of error here. If we reject a null hypothesis that is actually true, then we have made a type I error . On the other hand, if we retain the null hypothesis when it is in fact false, then we have made a type II error .
Remember how I said that statistical testing was kind of like a criminal trial? Well, I meant it. A criminal trial requires that you establish “beyond a reasonable doubt” that the defendant did it. All of the evidentiary rules are (in theory, at least) designed to ensure that there’s (almost) no chance of wrongfully convicting an innocent defendant. The trial is designed to protect the rights of a defendant: as the English jurist William Blackstone famously said, it is “better that ten guilty persons escape than that one innocent suffer.” In other words, a criminal trial doesn’t treat the two types of error in the same way~… punishing the innocent is deemed to be much worse than letting the guilty go free. A statistical test is pretty much the same: the single most important design principle of the test is to control the probability of a type I error, to keep it below some fixed probability. This probability, which is denoted α, is called the significance level of the test (or sometimes, the size of the test). And I’ll say it again, because it is so central to the whole set-up~… a hypothesis test is said to have significance level α if the type I error rate is no larger than α.
So, what about the type II error rate? Well, we’d also like to keep those under control too, and we denote this probability by β. However, it’s much more common to refer to the power of the test, which is the probability with which we reject a null hypothesis when it really is false, which is 1−β. To help keep this straight, here’s the same table again, but with the relevant numbers added:
| retain H 0 | reject H 0 | |
|---|---|---|
| H 0 is true | 1−α (probability of correct retention) | α (type I error rate) |
| H 0 is false | β (type II error rate) | 1−β (power of the test) |
A “powerful” hypothesis test is one that has a small value of β, while still keeping α fixed at some (small) desired level. By convention, scientists make use of three different α levels: .05, .01 and .001. Notice the asymmetry here~… the tests are designed to ensure that the α level is kept small, but there’s no corresponding guarantee regarding β. We’d certainly like the type II error rate to be small, and we try to design tests that keep it small, but this is very much secondary to the overwhelming need to control the type I error rate. As Blackstone might have said if he were a statistician, it is “better to retain 10 false null hypotheses than to reject a single true one”. To be honest, I don’t know that I agree with this philosophy – there are situations where I think it makes sense, and situations where I think it doesn’t – but that’s neither here nor there. It’s how the tests are built.