Which term refers to the ability to correctly identify true negatives?

• A. Negative predictive value
• B. Positive predictive value
• C. Sensitivity
• D. Specificity

Answer: (D)

This item is about how well a test identifies people who do not have the condition, focusing on correctly labeling them as negative. The term is specificity. It measures the proportion of actual negatives that the test correctly identifies as negative.

In other words, specificity = true negatives divided by the sum of true negatives and false positives. A high specificity means few false positives, so most people without the condition are correctly told they don’t have it. For example, if 100 people do not have the condition and the test marks 95 of them as negative and 5 as positive, the specificity is 95%.

Other measures describe different aspects. Sensitivity is about correctly identifying those who do have the condition (the true positive rate). Positive predictive value is the probability that someone who tests positive truly has the condition, while negative predictive value is the probability that someone who tests negative truly does not have it. Predictive values depend on how common the condition is in the population, whereas specificity is specifically about correctly identifying negatives regardless of prevalence.