Predictive Value Calculator (PPV/NPV)
Find a test's PPV and NPV.
Result
How it is calculated
From the four cells of a test result table the tool computes the predictive values: PPV = TP ÷ (TP + FP) × 100 and NPV = TN ÷ (TN + FN) × 100. TP are true positives, FP false positives, TN true negatives and FN false negatives, all entered as counts of people or samples. Counts cannot be negative, there must be at least one positive result (TP + FP) and one negative result (TN + FN), or nothing is shown. Results are percentages with two decimals. PPV answers how often a positive result is right, NPV how often a negative result is right. Both depend on how common the condition is in the group tested.
Worked example
A screening test gives 90 true positives, 30 false positives, 860 true negatives and 20 false negatives. Everyone who tested positive: 90 + 30 = 120, and 90 ÷ 120 = 75.00% are truly positive, which is the PPV. Everyone who tested negative: 860 + 20 = 880, and 860 ÷ 880 = 97.73% are truly negative, which is the NPV.
Common mistakes
- Mixing up PPV with sensitivity. Sensitivity divides TP by all people who have the condition (TP + FN), while PPV divides by all positive results (TP + FP).
- Carrying PPV from one population to another. The same test has a lower PPV when the condition is rarer, so figures from a clinic do not transfer to general screening.
- Entering percentages or rates in the four boxes. They must be counts from the same table.
- Putting the false negatives and false positives in the wrong cells, which swaps the meaning of the two results.
- Entering an empty group, such as no positives at all. PPV is then undefined and the tool shows nothing.
About this calculator
This calculator finds the positive predictive value (PPV) and negative predictive value (NPV) of a diagnostic test from a 2×2 confusion matrix of true positives, false positives, false negatives, and true negatives: PPV = TP/(TP+FP) is the probability that a positive result is truly positive, and NPV = TN/(TN+FN) is the probability that a negative result is truly negative.
Unlike sensitivity and specificity, which are fixed properties of a test, PPV and NPV depend heavily on disease prevalence in the population being tested — the same test performs very differently screening a low-risk general population versus a high-risk clinical group, which is why a positive result on a rare-disease screening test is often less reliable than intuition suggests.
Clinicians and diagnosticians interpreting lab and imaging test results, epidemiologists designing screening programs, and medical students learning Bayesian test interpretation all use PPV and NPV to translate a raw test result into a real probability of disease.
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