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

$\{\backslash rm\; specificity\}=\backslash frac\{\backslash rm\; number\backslash \; of\backslash \; true\backslash \; negatives\}$

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

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.

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

Specificity also relates to Cascading Style Sheets and the way CSS inherits values.

See also

• binary classification
• receiver operating characteristic
• sensitivity (tests)
• statistical significance
• Type I and type II errors
• Selectivity

External link

• Sensitivity and Specificity - Medical University of South Carolina

Statistics

Spezifität | Spésifisitas