Evidence-appraisal glossary
Positive predictive value
Positive predictive value is the chance that someone who tests positive actually has the condition. It is the proportion of positive results that are true positives. Unlike sensitivity and specificity, PPV depends heavily on how common the disease is in the tested group, so it falls sharply when the condition is rare.
Also called: PPV, precision.
Positive predictive value (PPV) answers the question a person with a positive result cares about: given this positive test, what is the probability the condition is truly present? It is true positives divided by all positive results (true plus false positives). PPV depends not only on the test's sensitivity and specificity but strongly on prevalence, the underlying frequency of the condition in the group tested. When reading a study, check the prevalence in the sample and whether it matches the real-world population, because a PPV from a high-prevalence clinic can be badly optimistic for general screening. For example, a test that is 99 percent sensitive and 99 percent specific still yields a PPV near 50 percent when only about 1 in 100 tested people have the disease, meaning half of positive results are false alarms. This is why a seemingly accurate test can mislead when applied to a low-risk population.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.