What is Positive Predictive Value (PPV)?

PPV is the probability that a patient with a positive test result actually has the disease. It depends not only on test sensitivity and specificity but critically on disease prevalence — the same test has a much lower PPV in a low-prevalence population.

What is Negative Predictive Value (NPV)?

NPV is the probability that a patient with a negative test result truly does not have the disease. High NPV is essential for screening tests where you want to reliably rule out disease.

Why does prevalence matter so much?

At very low prevalence, even a highly specific test produces many false positives relative to true positives, causing PPV to drop sharply. This is the classic base-rate fallacy in diagnostic testing.

What is the difference between sensitivity and PPV?

Sensitivity is a fixed property of the test (how well it detects true positives). PPV is a population-dependent outcome that tells you what a positive result means for a specific patient in a specific setting.

How is this calculated?

The calculator applies Bayes's theorem: PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + ((1 − Specificity) × (1 − Prevalence))]. NPV uses the complementary formula.

PPV and NPV Calculator

Enter sensitivity, specificity, and prevalence to instantly compute PPV, NPV, and the full 2×2 confusion matrix.

Test Parameters

Sensitivity (%) — true positive rate

Specificity (%) — true negative rate

Prevalence (%) — disease rate in population

Cohort size — for absolute counts

Positive Predictive Value

—

Probability disease present given positive test

Negative Predictive Value

—

Probability no disease given negative test

Derived Metrics

False Omission Rate

—

1 − NPV

False Discovery Rate

—

1 − PPV

Accuracy

—

(TP + TN) / N

F1 Score

—

Harmonic mean: sens & PPV

Likelihood Ratio (+)

—

Sens / (1 − Spec)

Likelihood Ratio (−)

—

(1 − Sens) / Spec

2×2 Confusion Matrix (cohort: 1,000)

	Disease Present	Disease Absent	Total
Test Positive	—	—	—
Test Negative	—	—	—
Total	—	—	—

TP = True Positives (top-left)

FP = False Positives (top-right)

FN = False Negatives (bottom-left)

TN = True Negatives (bottom-right)

Summary

Enter sensitivity, specificity, and prevalence to instantly compute PPV, NPV, and the full 2×2 confusion matrix.

How it works

Enter the test sensitivity (true positive rate) as a percentage.
Enter the test specificity (true negative rate) as a percentage.
Enter the disease prevalence in the population as a percentage.
Optionally enter a cohort size to see absolute counts in the 2×2 table.
PPV, NPV, and derived metrics update instantly as you type.

Use cases

Evaluate a screening test before rolling it out to a low-prevalence population.
Compare two diagnostics with different sensitivity/specificity trade-offs.
Teach Bayesian reasoning and the base-rate effect to medical students.
Estimate how often a positive COVID or strep test is a true positive.
Plan research studies by modeling expected true/false positive rates.
Understand why a highly sensitive test can still have poor PPV at low prevalence.

Frequently Asked Questions

Last updated: 2026-09-22 · Reviewed by Nham Vu