Evidence-appraisal glossary
Negative predictive value
Negative predictive value is the chance that someone who tests negative truly does not have the condition. It is the proportion of negative results that are true negatives. Like PPV, it depends on how common the disease is: NPV tends to be high when the condition is rare in the tested group.
Also called: NPV.
Negative predictive value (NPV) answers what a person with a negative result wants to know: given this negative test, what is the probability the condition is truly absent? It is true negatives divided by all negative results (true negatives plus false negatives). NPV combines the test's sensitivity and specificity with prevalence, the frequency of the condition in the tested group. When reading a study, look at the prevalence in the sample and whether it matches the intended population, because a high NPV in a low-prevalence sample can shrink when the same test is used in a higher-risk group. For example, when a condition affects only about 1 in 100 people tested, even a moderately accurate test can post an NPV above 99 percent simply because most people genuinely do not have the disease. That high number reflects rarity as much as test quality, so it should be read alongside prevalence and the test's sensitivity rather than on its own.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.