Sensitivity And Specificity Calculator

Sensitivity and Specificity Formulas:

\[ Sensitivity = \frac{TP}{TP + FN} \] \[ Specificity = \frac{TN}{TN + FP} \]

Sensitivity:

Specificity:

Unit Converter ▲

Unit Converter ▼

From:	To:

1. What are Sensitivity and Specificity?

Sensitivity (true positive rate) measures the proportion of actual positives correctly identified by a test. Specificity (true negative rate) measures the proportion of actual negatives correctly identified by a test. These are fundamental metrics for evaluating diagnostic tests.

2. How Does the Calculator Work?

The calculator uses these formulas:

\[ Sensitivity = \frac{TP}{TP + FN} \] \[ Specificity = \frac{TN}{TN + FP} \]

Where:

\( TP \) — True Positives (correctly identified cases)
\( FN \) — False Negatives (missed cases)
\( TN \) — True Negatives (correctly identified non-cases)
\( FP \) — False Positives (incorrectly identified cases)

Explanation: Sensitivity indicates how good the test is at detecting true cases, while specificity indicates how good it is at avoiding false alarms.

3. Importance of These Metrics

Details: These metrics help evaluate diagnostic tests. High sensitivity is important when missing a case is dangerous. High specificity is important when false alarms have serious consequences.

4. Using the Calculator

Tips: Enter counts from a 2x2 contingency table. All values must be non-negative integers. The sum (TP+FN) and (TN+FP) must be greater than zero.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between sensitivity and precision?
A: Sensitivity (recall) focuses on correctly identifying true cases, while precision focuses on the proportion of positive identifications that were correct.

Q2: Can a test have 100% sensitivity and specificity?
A: In theory yes, but in practice this is extremely rare as most tests involve trade-offs between these metrics.

Q3: Which is more important - sensitivity or specificity?
A: It depends on context. For life-threatening diseases, high sensitivity may be prioritized. For conditions with risky treatments, high specificity may be more important.

Q4: How do prevalence affect these metrics?
A: Sensitivity and specificity are prevalence-independent, but predictive values (PPV/NPV) are affected by prevalence.

Q5: What are good values for sensitivity and specificity?
A: Generally >80% is acceptable, >90% is good, and >95% is excellent, but this varies by field and application.