Sensitivity/Specificity Calculator
How Good Is a Test at Finding What It's Looking For
Every diagnostic test makes two kinds of errors: missing a condition that's actually present, and flagging a condition that isn't there. Sensitivity and specificity separate those two failure modes into distinct numbers, drawn from a 2×2 table of test results against confirmed truth, so a test's real-world reliability can be evaluated on paper before it's ever used to make a clinical decision.
The Formula
Specificity = TN ÷ (TN + FP) × 100
The Confusion Matrix
| Condition present | Condition absent | |
|---|---|---|
| Test positive | True Positive (TP) | False Positive (FP) |
| Test negative | False Negative (FN) | True Negative (TN) |
Sensitivity looks only at the "condition present" column (how many were correctly caught); specificity looks only at the "condition absent" column (how many were correctly cleared).
Why Both Numbers Are Needed
- Screening test design — a screening test is often built for high sensitivity, accepting more false positives, so it rarely misses a true case, with confirmation testing to follow.
- Confirmatory testing — a follow-up test is often built for high specificity, to avoid falsely confirming a condition that isn't present.
- Comparing competing tests — two tests with the same overall accuracy can have very different sensitivity/specificity trade-offs, which matters depending on the cost of each type of error.
How to Use This Calculator
- Enter the number of True Positives (TP) — correctly identified cases.
- Enter the number of False Negatives (FN) — missed cases.
- Enter the number of True Negatives (TN) — correctly cleared cases.
- Enter the number of False Positives (FP) — incorrectly flagged cases.
- Select Calculate to see sensitivity and specificity as percentages.
Related Calculations
Use the PPV/NPV Calculator to see how these numbers translate into the probability a given result is correct, or the Likelihood Ratio Calculator for a single combined diagnostic value.