Which term describes the probability of a positive test result for patients who do not have the disease?

The term that accurately describes the probability of a positive test result for patients who do not have the disease is known as the false positive rate. This metric indicates how frequently a test incorrectly identifies non-diseased individuals as positive cases. Specifically, it is calculated as the number of false positives divided by the sum of false positives and true negatives. In essence, a higher false positive rate suggests that the test is less effective at correctly identifying those who do not have the disease, which could lead to unnecessary anxiety, further testing, or treatment for those incorrectly identified.

In contrast, specificity measures the ability of a test to correctly identify those without the disease by calculating the proportion of true negatives out of the total number of actual negatives. Sensitivity, also known as the true positive rate, refers to the test's ability to identify true positives among those who have the disease, while the true positive rate is a synonym for sensitivity. Thus, while specificity and sensitivity are important statistics related to test performance, they do not describe the probability of a positive test result for individuals who actually do not have the disease.