1. What Is Specificity?

Specificity (also called true negative rate) measures the proportion of actual negatives that are correctly identified as such. It's a key metric for evaluating diagnostic tests.

2. How Specificity Is Calculated

The specificity formula is:

Where:

• True Negatives (TN) - Correctly identified negative cases
• False Positives (FP) - Negative cases incorrectly identified as positive

3. Clinical Importance

Details: High specificity tests are important when the cost of false positives is high (e.g., when subsequent testing is invasive or expensive).

4. Using The Calculator

Instructions: Enter the true negatives and false positives from your confusion matrix. Both values must be non-negative integers.

5. Frequently Asked Questions

Q1: What's a good specificity value?
A: Typically >0.9 is excellent, 0.8-0.9 is good, but depends on the clinical context.

Q2: How does specificity relate to sensitivity?
A: Sensitivity measures true positive rate, while specificity measures true negative rate - often a trade-off exists between them.

Q3: What if my denominator is zero?
A: Specificity is undefined when there are no negative cases (TN + FP = 0).

Q4: How is specificity different from precision?
A: Precision focuses on positive predictions (TP/(TP+FP)), while specificity focuses on negative cases.

Q5: When should I prioritize specificity?
A: When the consequences of false positives are severe (e.g., psychological distress from false diagnosis).