Definitions for sensitivity and specificity
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Sensitivity and specificity
Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function. Sensitivity measures the proportion of actual positives which are correctly identified as such. Specificity measures the proportion of negatives which are correctly identified as such. These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitive and 100% specific; however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate. For any test, there is usually a trade-off between the measures. For example: in an airport security setting in which one is testing for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys, in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard. This trade-off can be represented graphically as a receiver operating characteristic curve.
U.S. National Library of Medicine
Sensitivity and Specificity
Measures for assessing the results of diagnostic and screening tests. Sensitivity represents the proportion of truly diseased persons in a screened population who are identified as being diseased by the test. It is a measure of the probability of correctly diagnosing a condition. Specificity is the proportion of truly nondiseased persons who are so identified by the screening test. It is a measure of the probability of correctly identifying a nondiseased person. (From Last, Dictionary of Epidemiology, 2d ed)
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