In binary testing, e.g. a medical diagnostic test for a certain disease, specificity is the proportion of true negatives of all the negative samples tested, that is

In Information Retrieval, specificity is called precision.

For a test to determine who has a certain disease, a specificity of 100% means that all people labeled as sick are actually sick.

Specificity alone does not tell us all about the test, because a 100% specificity can be trivially achieved by labeling all test cases negative. Therefore, we also need to know the sensitivity of the test.

F-measure can be used as a single measure of performance of the test. The F-measure is the geometric mean of sensitivity and specificity:

F = 2 * precision * recall / (precision + recall).

A test with a high specificity has a low Type I error.

Sensitivity is not the same as the positive predictive value defined as

which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.

See Also

• binary classification
• receiver operating characteristic
• sensitivity (tests)
• statistical significance

External link

• Sensitivity and Specificity - Medical University of South Carolina