What is the Difference Between Sensitivity and Specificity?

Sensitivity and specificity are measures of a test's ability to correctly classify a person as having or not having a particular condition or disease. They are inversely proportional, meaning that as one increases, the other decreases.

• Sensitivity refers to a test's ability to designate an individual with the disease as positive. A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed. Sensitivity is calculated as:

$$Sensitivity = \frac{Number\ of\ true\ positives}{Number\ of\ true\ positives + Number\ of\ false\ negatives}$$

• Specificity refers to a test's ability to designate an individual who does not have the disease as negative. A highly specific test means that there are few false positive results. Specificity is calculated as:

$$Specificity = \frac{Number\ of\ true\ negatives}{Number\ of\ true\ negatives + Number\ of\ false\ positives}$$

It is desirable to have a test that is both highly sensitive and highly specific. However, this is often not possible, and a trade-off between sensitivity and specificity may be necessary. The choice between sensitivity and specificity depends on the context and the goals of the test. For example, in disease screening, it might be more important to have high sensitivity to detect as many cases as possible, even if it means having more false positives.

Comparative Table: Sensitivity vs Specificity

Here is a table comparing the differences between sensitivity and specificity:

In summary, sensitivity measures how well a test identifies people with a condition, while specificity measures how well a test identifies people without the condition. Both measures are important for evaluating the performance of a diagnostic test, but they serve different purposes and are inversely related.