Sensitivity vs specificity: which statement is true?

Sensitivity vs specificity: which statement is true?

• A. Sensitivity measures the ability to identify true positives; Specificity measures the ability to identify true negatives.
• B. Sensitivity measures false positives; Specificity measures false negatives.
• C. Sensitivity measures negatives; Specificity measures positives.
• D. Sensitivity and Specificity measure predictive values.

Answer: (A)

Sensitivity and specificity are measures of a diagnostic test’s accuracy. Sensitivity is the ability to correctly identify those who have the disease (true positives), while specificity is the ability to correctly identify those who do not have the disease (true negatives). So the statement is true because it directly matches these definitions: sensitivity reflects catching true positives, and specificity reflects correctly labeling true negatives. To see why the other ideas are not correct: false positives come from a test’s tendency to flag disease in healthy people, which relates to 1 minus specificity, not to sensitivity. False negatives come from missing disease in those who are actually diseased, which relates to 1 minus sensitivity, not to the raw sensitivity itself. Predictive values (positive and negative predictive values) tell you how likely a positive or negative result is correct in a given population, and they depend on disease prevalence, not on sensitivity or specificity alone.

Sensitivity and specificity are measures of a diagnostic test’s accuracy. Sensitivity is the ability to correctly identify those who have the disease (true positives), while specificity is the ability to correctly identify those who do not have the disease (true negatives).

So the statement is true because it directly matches these definitions: sensitivity reflects catching true positives, and specificity reflects correctly labeling true negatives.

To see why the other ideas are not correct: false positives come from a test’s tendency to flag disease in healthy people, which relates to 1 minus specificity, not to sensitivity. False negatives come from missing disease in those who are actually diseased, which relates to 1 minus sensitivity, not to the raw sensitivity itself. Predictive values (positive and negative predictive values) tell you how likely a positive or negative result is correct in a given population, and they depend on disease prevalence, not on sensitivity or specificity alone.