TimelyScholarFree Tools › Sensitivity & Specificity Calculator
Enter true and false positives and negatives to get sensitivity, specificity, predictive values, likelihood ratios and accuracy, each with a 95% confidence interval.
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| Condition present | Condition absent | |
|---|---|---|
| Test positive | True positives | False positives |
| Test negative | False negatives | True negatives |
| Sensitivity | 90.0% (95% CI 82.6% to 94.5%) |
|---|---|
| Specificity | 85.0% (95% CI 79.4% to 89.3%) |
| Positive predictive value (PPV) | 75.0% (95% CI 66.6% to 81.9%) |
| Negative predictive value (NPV) | 94.4% (95% CI 90.1% to 97.0%) |
| Positive likelihood ratio (LR+) | 6.00 |
| Negative likelihood ratio (LR−) | 0.118 |
| Diagnostic odds ratio | 51.00 |
| Accuracy | 86.7% (95% CI 82.4% to 90.1%) |
| Prevalence in this sample | 33.3%PPV and NPV depend on prevalence |
| Youden's index | 0.750 |
Confidence intervals for proportions use the Wilson score method.
True positives, false positives, false negatives and true negatives.
Sensitivity, specificity, PPV, NPV and likelihood ratios.
Sensitivity is the proportion of people with the condition who test positive; specificity is the proportion without it who test negative. They describe the test itself.
Positive and negative predictive values tell you how likely a result is to be correct, and they depend on how common the condition is in the population tested. Likelihood ratios combine sensitivity and specificity and do not depend on prevalence.
They depend on prevalence. The same test has a lower PPV where the condition is rare.
The further LR+ is above 1, the more a positive result raises the probability of the condition; the closer LR− is to 0, the more a negative result lowers it. Values near 1 change the probability very little.