Sensitivity (tests) - Definition and Overview

The sensitivity of a binary classification test or algorithm, such as a blood test to determine if a person has a certain disease, or an automated system to detect faulty products in a factory, is a parameter that expresses something about the test's performance. The sensitivity of such a test is the proportion of those cases having a positive test result of all positive cases (eg, people with the disease, faulty products) tested.

<math>{\rm sensitivity}=\frac{\rm number\ of\ true\ positives}{{\rm number\ of\ true\ positives}+{\rm number\ of\ false\ negatives}}.<math>

A sensitivity of 100% means that all sick people or faulty products were recognized as such, but it alone doesn't tell us all about the test, as a 100% sensitivity can be trivially achieved by labeling all test cases positive, despite their true status. For more information see binary classification. See also specificity.

In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.

In the context of information retrieval, the concept of sensitivity is also known as recall.