Positive Predictive Value, PPV
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
The positive predictive value is the probability that a person has a condition given that they tested positive, equal to true positives divided by all positives. The core claim is that this value depends on prevalence as well as on test accuracy, so a highly sensitive and specific test can still produce mostly false positives when applied to a low-prevalence population. The qualification is that the predictive value is a property of the test in a population rather than of the test alone, so it changes when the population or the testing criteria change.
SCAFFOLDING EFFECT
Reduce cognitive load
- Result reading: ask what proportion of the positives in this population are actually true. - Prior check: establish the base rate before interpreting any single positive test result. - Screening policy: compare the harm of false positives against the benefit of early detection.
Anchor fast decisions
Even a small false positive rate generates many false positives when the number of healthy people tested is large, because that rate applies to a much bigger group. True positives, by contrast, come only from the small number of people who actually have the condition. As prevalence falls, the false positives crowd out the true ones, so the share of correct positives collapses even though the test itself has not changed.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Positive_and_negative_predictive_valuesverified
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