** Sensitivity vs Specificity
**

Sensitivity and specificity are two terms coming under statistical testing. Depending on the nature of the study, the importance of the two may vary. The ideal test should be able to deliver results with 100% sensitivity and 100% specificity but in a practical application it is difficult to be achieved. In most incidents, a trade between the two is essential to build a reasonable foundation to the reliability of the test results.

**Sensitivity**

Sensitivity is also called the recall rate. This measures the probability of actual positives. In other words, this test feature is more focused on identifying the sample members who are actually positive towards the tested property. For an instance let’s take a test which finds how many patients are actually suffering from a certain disease, then we can say that we are expecting the probability of positive responses towards the tested property- “ill”; therefore, such measurements are focusing on sensitivity. Sensitivity can be shown by a simple equation;

*Sensitivity = Number of true positives (correctly identified)/[ Number of true positives+ Number of false negatives (incorrectly rejected)]*

Trying to achieve 100% sensitivity in a practical test is pointless because it eliminates the incorrectly rejected lot. Therefore, the effort is to reach very high sensitivity and a high sensitive test can be considered quite reliable. One should not think sensitivity means precision. Precision delivers a ratio of positive results to the false positive results whereas sensitivity is a measure of the ratio of actual positives to the total of positives the test measured, including the indirectly counted ones.

**Specificity**

Specificity is also known as true negative rate. This measures the probability of actual negatives. The focus of this measurement is to find out the sample members who are actually negative towards the tested property. Taking the same example, where people are tested for suffering from a certain disease; if the test method is changed to be focusing on who are the people who does not have the disease, then we can say that the test measure specificity. Therefore, it is clear that what specificity does is confirming how many are negative towards the tested property. Specificity can also be easily put to an equation;

*Specificity = Number of true negatives (correctly rejected)/ Number of true negatives+ Number of false positives (incorrectly identified)*

Specificity is very important in medical testing and chemical testing. In medical testing confirming that a person does not have the disease is more important than detecting a person has it. Because when the positive response is taken in to consideration there is no assurance on the degree of disease, for it simple states the person is positive. But knowing a person has no disease is more decided and strong result. It is same for chemical testing, where finding that certain substances are present is a weak result than finding its absence. Both these statistical properties are important, and it is crucial to decide which should be traded off for which.

**What is the difference between Sensitivity and Specificity?**

• Sensitivity measures the probability of something being tested “positive”

• Specificity measures the probability of something being tested “negative”