Which statement best defines sensitivity in diagnostic testing?

• A. The probability that someone with the disease tests positive.
• B. The probability that someone without the disease tests negative.
• C. The probability that a test is positive in the general population.
• D. The probability of a false positive.

Answer: (A)

Sensitivity is the test’s ability to correctly identify those who actually have the disease. It’s the true positive rate: among people with the disease, what fraction tests positive? That’s exactly what the statement describes—the probability a person with the disease tests positive.

Think of it this way: if sensitivity is high, few cases are missed (few false negatives). For example, if 20 people truly have the disease and the test has 90% sensitivity, 18 test positive (true positives) and 2 test negative (false negatives). This illustrates why sensitivity focuses on diseased individuals and positive results.

The other ideas relate to different test properties: specificity is the probability that someone without the disease tests negative; the probability of a positive in the general population isn’t a performance metric of the test itself; and the probability of a false positive is tied to 1 minus specificity, not sensitivity.