In statistical analysis, there are two types of errors: Type I is a false positive, Type II is a false negative. I still get these mixed up. I spent five minutes trying to think of a clever mnemonic and I couldn’t come up with anything. Any ideas?
A nice summary from wikipedia (below the fold):
When an observer makes a Type I error, they are observing a statistical difference when in truth there is none (rejecting a null hypothesis when it is actually true). For example, a pregnancy test with a positive result (indicating that the person taking the test is pregnant) has produced a false positive in the case where the person is not pregnant. A Type II error, or a “false negative”, is the error of failing to reject a null hypothesis when the alternative hypothesis is the true state of nature. For example, a type II error occurs if a pregnancy test reports negative when the person is, in fact, pregnant.