Calculate post test probability with statistical precision, sample distributions, and inference data.
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Post-Odds = Pre-Odds × LR, P(D|+) = Post-Odds / (1 + Post-Odds), LR+ = Sens / (1 - Spec)P(D)SensSpecLR+P(D|+)“A patient tests positive on a rapid antigen test for a condition with 5.0% community prevalence. The test has 85% sensitivity and 98% specificity.”
Convert prior probability to odds format.
Divide sensitivity by false positive rate (1 - specificity).
Multiply prior odds by LR+ and convert back to probability.
A positive test increases the probability of condition presence from 5% to 69.10%, warranting clinical follow-up.
| Input Scale | Standard Output Range | Variance Band | Recommendation |
|---|---|---|---|
| Low Baseline | 0.00 – 25.00 | ±1.5% | Optimal for conservative estimations |
| Median / Standard | 25.00 – 100.00 | ±0.8% | Benchmark standard for commercial workflows |
| Elevated / High | 100.00 – 500.00 | ±0.4% | Recommended for high-throughput modeling |
| Extreme Maximum | > 500.00 | ±0.1% | Verify input constraints for boundary saturation |
Provide your numeric inputs into the designated fields in Post Test Probability Calculator.
Observe real-time recalculations as values update without requiring a manual submit button.
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Sensitivity only measures P(Positive | Diseased). Post-test probability measures P(Diseased | Positive), which depends heavily on background disease prevalence. In rare conditions, false positives from the healthy majority can outnumber true positives.
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