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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 (e.g., people with the disease, faulty products) tested.

- $ {\rm sensitivity}=\frac{\rm number\ of\ true\ positives}{{\rm number\ of\ true\ positives}+{\rm number\ of\ false\ negatives}}. $

A sensitivity of 100% means that all sick people or faulty products are recognized as such.

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

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

- $ \frac{\rm number\ of\ true\ positives}{{\rm number\ of\ true\ positives}+{\rm number\ of\ false\ positives}} $

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

In information retrieval, positive predictive value is called **precision**, and
**sensitivity** is known as **recall**.

*F-measure* can be used as a single measure of performance of the test. The F-measure is the harmonic mean of precision and recall:

- $ F = 2 \times {\rm precision} \times {\rm recall} / ({\rm precision} + {\rm recall}). $

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 information retrieval, sensitivity is called **recall**.