Which statement defines specificity in diagnostic testing?

• A. True negatives / (true negatives + false positives)
• B. True positives / (true positives + false negatives)
• C. True positives / (true positives + false positives)
• D. True negatives / (true negatives + false negatives)

Answer: (A)

Specificity is the proportion of people without the disease who test negative. It is calculated as true negatives divided by all people without the disease, TN/(TN+FP). This directly measures how well the test avoids false positives, so a high specificity means a negative result is reliable for ruling out disease. For example, if 100 disease-free individuals are tested and 90 are correctly identified as negative while 10 are incorrectly identified as positive, the specificity is 90%. The other formulas touch on different concepts: sensitivity uses true positives over (true positives + false negatives) and tells how well the test detects disease; positive predictive value uses true positives over (true positives + false positives) and depends on disease prevalence; negative predictive value uses true negatives over (true negatives + false negatives) and tells how well a negative result rules out disease.