﻿ Sensitivity Calculator ﻿

# Sensitivity Calculator

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This sensitivity calculator calculates the sensitivity of a data sample.

Sensitivity refers to the test's ability to correctly identify patients who have a certain condition (disease, illness, etc).

In a medical example for a disease, for example, the sensitivity is the ability of a test to correctly identify patients who have a disease.

Therefore, the formula to calculate sensitivity is, number of true positives/(number of true positives + number of false negatives).

The number of true positives refers to the number of people that the test identified had the disease.

The number of false negatives refers to the number of people that the test identified did not have the disease who actually had the disease. Therefore, the test missed this group of people (it got it wrong).

The ratio of the number of true positives over the number of true positives plus false negatives gives the ratio of how precise, or sensitive, a test is.

The higher the sensitivity of a test, the more precise it is. Therefore, the more reliable it is.

The sensitivity of a test shows us how reliable it is or in layman terms, how often the tests accurately diagnoses a patient correctly has a disease.

The specificity is the exact opposite of sensitivity. The specificity shows how accurately a test can predict that a patient does not have a disease (true negatives rate). While the sensitivity tells the true positives rate, the specificity gives the true negatives rate (how good the test it is at determining that patients do not have a condition who actually don't have a condition; in other words, how good it is at not giving false positives).

To use this calculator, a user simply enters in the number of true positives, along with the number of false negatives, and clicks the "Calculate" button. The resultant sensitivity (in decimals and percentage) will then be automatically computed and shown.

If you have the number of true positives and the total amount of the sample (who has a disease, for instance), simply take the total amount and subtract the number of true positives from it; this yields the number of false negatives. Then plug these values into the text fields.

### Example

A test correctly identified that 34 patients had neurofibromatosis out of 45 people. It didn't correctly identify that 11 patients had the disease who actually did have it. What is the sensitivity of this test?

Sensitivity= Number of True Positives/(Number of True Positives + Number of False Negatives)= 36/(36+9)= 36/45= 0.8 or 80%

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