A false positive occurs when a test or classification system incorrectly labels a negative case as positive. In other words, the test indicates the presence of a condition or outcome that isn’t actually there. It is one of the four outcomes in a confusion matrix, which categorizes test results into: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).
False positives play a key role in calculating several accuracy measures, including specificity, positive predictive value (PPV), and false positive rate (FPR). Each of these metrics helps evaluate a different aspect of a test’s accuracy.
For example, a false positive result on a screening test for colon cancer means that a healthy person is incorrectly told they might have cancer. This can lead to unnecessary emotional distress, invasive follow-up procedures like colonoscopies, and increased healthcare costs—all without any medical benefit.
