Post-Test Probability Calculator

Find post-test probability from prevalence, sensitivity and specificity.

Post-test prob (positive) (%) 33.333

Post-test prob (negative) (%) 99.708

Formula: Bayes' theorem on prevalence and test accuracy

Step-by-step with your numbers:

1. Values used:

2. Prevalence (pre-test) = 5 %

3. Sensitivity = 95 %

4. Specificity = 90 %

6. Post-test prob (positive) = 33.333%

7. Post-test prob (negative) = 99.708%

Did we solve your problem today?

After a test result, Bayes' theorem updates the probability of disease.

The math behind it

A positive result's true value (PPV) depends heavily on prevalence — the false-positive paradox.

5% prevalence, 95% sensitivity, 90% specificity → a positive means only ~33% chance of disease.

Why so low after a positive?

When disease is rare, false positives outnumber true positives.