information:classification, quality:correction
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. For example, an innocent person may be convicted. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. For example: a guilty person may be not convicted. Much of statistical theory revolves around the minimization of one or both of these errors, though the complete elimination of either is an impossibility if the outcome is not determined by a known, observable causal process. By selecting a low threshold (cut-off) value and modifying the alpha (α) level, the quality of the hypothesis test can be increased. The knowledge of type I errors and type II errors is widely used in medical science, biometrics and computer science. Intuitively, type I errors can be thought of as errors of commission. For instance, consider a study where researchers compare a drug with a placebo. If the patients who are given the drug improve more than the patients given the placebo by chance, it may appear that the drug is effective, but in fact the opposite is true. By contrast, type II errors are errors of omission. In the example above, if the patients who got the drug did not get better at a higher rate than the ones who got the placebo and this was a random fluke, that would be a type II error.