Negative predictive value is defined as

• A. Sensitivity
• B. Specificity
• C. Positive Predictive Value
• D. Negative Predictive Value

Answer: (D)

Negative predictive value is the probability that someone truly does not have the disease when their test result is negative. In other words, among all people who test negative, what fraction are actually disease-free? It’s calculated as true negatives divided by the total number of negative results (true negatives plus false negatives).

This is different from sensitivity (how good the test is at catching disease when it’s present) and specificity (how good the test is at ruling out disease when it’s not present). It’s also distinct from positive predictive value, which is the probability of disease given a positive test.

NPV depends on disease prevalence: with very low prevalence, most negatives are true negatives and NPV is high; as prevalence rises, NPV falls.

Example: in 1000 people with 10% disease prevalence, a test with 90% sensitivity and 95% specificity yields 855 true negatives and 10 false negatives among the non-diseased group, for a total of 865 negative results. NPV ≈ 855/865 ≈ 99%.