灵敏度和特异度是统计学中用来表征二项分类测试特征的数据。其中灵敏度是真阳性与真阳性+假阴性的比值；特异度是=真阴性与真阴性+假阳性的比值。

在一个100人的样本中，有10人事实上患有A病（阳性），经过检测后，9人判定患有A病（真阳性），而1人判定并不患有A病（假阴性）； 另外的90人被认为并不患有A病（阴性），然后经过检测后，其中的5人被判定患有A病（假阳性），另外的85人判定不患有A病（真阴性）。

灵敏度=真阳性/（真阳性+假阴性）=9/(9+1)=90%;

特异度=真阴性/（真阴性+假阳性）=85/(85+5)=94.4%.

Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function:

Thus sensitivity quantifies the avoiding of false negatives, as specificity does for false positives. For any test, there is usually a trade-off between the measures. For instance, 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 (low specificity), in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity). This trade-off can be represented graphically as a receiver operating characteristic curve. A perfect predictor would be described as 100% sensitive (e.g., all sick are identified as sick) and 100% specific (e.g., no healthy are identified as sick); however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate.