For example, if a test has 95% sensitivity and 95% specificity (considered very good), then: Predictive values are based upon the prevalence of disease in a population. For a test with 99% sensitivity and 99% specificity, here are positive predictive values for different levels of prevalence.

## What does a specificity of 50% mean?

Specificity: From the 50 healthy people, the test has correctly pointed out all 50. Therefore, its specificity is **50 divided by 50 or 100%**. According to these statistical characteristics, this test is not suitable for screening purposes; but it is suited for the final confirmation of a disease.

## How do you interpret specificity?

Specificity is **the proportion of people WITHOUT Disease X that have a NEGATIVE blood test**. A test that is 100% specific means all healthy individuals are correctly identified as healthy, i.e. there are no false positives.

## What’s the difference between specificity and sensitivity?

Sensitivity: the ability of a test to correctly identify patients with a disease. Specificity: the ability of a test to **correctly identify people without the disease**. True positive: the person has the disease and the test is positive. True negative: the person does not have the disease and the test is negative.

## What are true positives and false positives?

A **true positive is an outcome where the model correctly predicts the positive class**. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.

## Is it better to have high sensitivity or high specificity?

A **highly sensitive** test means that there are few false negative results, and thus fewer cases of disease are missed. The specificity of a test is its ability to designate an individual who does not have a disease as negative. A highly specific test means that there are few false positive results.

## How does sensitivity affect positive predictive value?

Sensitivity is **the percentage of true positives** (e.g. 90% sensitivity = 90% of people who have the target disease will test positive). Specificity is the percentage of true negatives (e.g. 90% specificity = 90% of people who do not have the target disease will test negative).