A false negative, also called a miss, exists when a test reports, incorrectly, that a signal was not detected when, in fact, was present. For example, radar not detecting an enemy air plane when an enemy air plane was present within the radar scanned area.

Biometric Example

This is problematic when it happens in biometric scans, such as retina scans or facial recognition, when the scanner incorrectly identifies someone as not matching a known person, when in actually, it is the same person whose scan was in the system.

When developing such software or hardware there is always a tradeoff between false negatives (in which an actual match is not detected) and false positives (in which an incorrect match is detected). In the language of statistical hypothesis testing, this is a question of balancing the risk of Type II errors (false negatives which fail to reject the null hypothesis when it is false) against Type I errors (false positives which reject the null hypothesis when it is true).

Usually there is some trigger value of how close a match to a given sample must be achieved before the algorithm reports a match. The higher this trigger value is, the more similar an object has to be to be detected and the fewer false positives will be created.

Medical Example

False negatives are also a significant issue in medical testing. In some cases, there are two or more (often many) tests that could be used, one of which is simpler and less expensive, but less accurate, than the other. For example, the simplest tests for HIV and hepatitis in blood have a significant rate of false positives. These tests are used to screen out possible blood donors, but more expensive and more precise tests are used in medical practice , to determine whether a person is actually infected with these viruses.

False negatives in medical testing provide false, incorrect reassurance to both patients and physicians that patients are free of disease which is actually present. This in turn leads to people receiving inappropriate understanding and a lack of better advice and treatment to better protect their interests. A common example is relying on cardiac stress tests to detect coronary atherosclerosis, even though cardiac stress tests are known to only detect limitations of coronary artery blood flow due to advanced stenosis.

False negatives produce serious and counterintuitive problems, especially when the condition being searched for is common. If a test with a false negative rate (termed specificity) of only 10%, is used to test a population with a true occurrence rate of 70%, many of the "negatives" detected by the test will be falsely incorrect. See below:

Bayes' Theorem

The probability that an observed negative result is a false negative versus a true negative may be calculated (and the problem of false negatives demonstrated) using Bayes' theorem. The key concept of Bayes' theorem is that the true rates of both false positives and false negatives is not a function of the accuracy of the test primarily. Instead the true rates of both false positives and false negatives are the result of a combination of both test accuracy and the actual rate within the population being examined. Often the more powerful issue is the actual rates of the condition within the sample being tested.

See Also: False positive