Define sensitivity and specificity.

• A. Sensitivity is the true negative rate; Specificity is the true positive rate.
• B. Sensitivity is the true positive rate; Specificity is the true negative rate.
• C. Sensitivity is the rate of false positives; Specificity is the rate of false negatives.
• D. Sensitivity and Specificity measure predictive values.

Answer: (C)

Sensitivity is the true positive rate: among people who truly have the condition, the proportion that the test correctly identifies as positive. Specificity is the true negative rate: among people who do not have the condition, the proportion that the test correctly identifies as negative. The other statements mix up these ideas or point to related metrics. For example, swapping true positives with true negatives confuses the two, and describing the measures as predictive values ignores how prevalence affects those values. Predictive values depend on how common the condition is, while sensitivity and specificity are intrinsic properties of the test’s ability to classify disease status.