Sensitivity is best described as:

Sensitivity is best described as:

• A. How well does the test detect true cases (focuses on true positives).
• B. How well does the test exclude non-cases (true negatives).
• C. The proportion of all results that are true positives.
• D. The proportion of true negatives among those without the disease.

Answer: (A)

Sensitivity is about catching disease when it’s really present. It’s the true positive rate—the proportion of actual disease cases that the test correctly identifies as positive. In formula form, sensitivity = true positives / (true positives + false negatives). This means a highly sensitive test minimizes false negatives, so fewer people with the disease are missed. The other descriptions pull in different ideas. Excluding non-cases is about specificity, not sensitivity, and describes how well the test identifies those without the disease. Saying that a large share of all results are true positives mixes in how often the disease occurs and other error types, which isn’t what sensitivity measures. So the description focusing on detecting true cases among those who have the disease is the best fit.

Sensitivity is about catching disease when it’s really present. It’s the true positive rate—the proportion of actual disease cases that the test correctly identifies as positive. In formula form, sensitivity = true positives / (true positives + false negatives). This means a highly sensitive test minimizes false negatives, so fewer people with the disease are missed.

The other descriptions pull in different ideas. Excluding non-cases is about specificity, not sensitivity, and describes how well the test identifies those without the disease. Saying that a large share of all results are true positives mixes in how often the disease occurs and other error types, which isn’t what sensitivity measures. So the description focusing on detecting true cases among those who have the disease is the best fit.