Final answer:
Specificity in an ROC curve represents the ability of a test to correctly identify non-diseased individuals as negative, calculated as the ratio of true negatives to total non-disease cases. High specificity indicates a lower chance of false positives.
Step-by-step explanation:
For an ROC curve, the measure called specificity reflects the test’s ability to correctly indicate an individual does not have the disease when indeed they do not. Specifically, specificity is the ratio of true negatives to total event non-occurrences, which is equivalent to the relative frequency of true negatives among all those who are not affected by the condition the test is screening for. High test specificity suggests that there is a low probability of a false positive test result, meaning that the likelihood of incorrectly indicating the presence of a disease in a non-afflicted individual is low. It’s important to distinguish specificity from sensitivity, as sensitivity concerns the probability of correctly identifying those with the condition (true positives).