Specificity Calculator
Calculate test specificity, true negative rate, and diagnostic performance metrics
Calculate Test Specificity
Healthy people correctly identified as negative
Healthy people incorrectly identified as positive
Specificity Results
Formula: Specificity = TN / (TN + FP)
Calculation: 0 / (0 + 0) = 0.00%
False Positive Rate: 100.00%
Specificity Interpretation Guide
High Specificity (≥85%)
- • Excellent at correctly identifying negative cases
- • Few false positives
- • Good for confirmatory tests
- • Positive result is meaningful
Low Specificity (<70%)
- • Incorrectly identifies many negatives as positive
- • Many false positives
- • Not ideal for confirmation
- • Positive result less reliable
Quick Reference
Specificity
True Negative Rate
TN / (TN + FP)
Sensitivity
True Positive Rate
TP / (TP + FN)
PPV
Positive Predictive Value
TP / (TP + FP)
NPV
Negative Predictive Value
TN / (TN + FN)
Clinical Applications
Confirmatory diagnostic tests
Rule-in test validation
False positive minimization
Binary classifier evaluation
Diagnostic accuracy assessment
Understanding Specificity in Diagnostic Testing
What is Specificity?
Specificity, also known as the true negative rate, measures a test's ability to correctly identify negative cases. It answers the question: "Of all people who don't have the condition, what percentage does the test correctly identify as negative?"
Clinical Interpretation
- •High specificity: Excellent for ruling in disease when positive
- •Low specificity: Many healthy people will test positive (false positives)
- •Ideal for confirmatory tests to avoid unnecessary treatments
Mathematical Foundation
Specificity = TN / (TN + FP)
= True Negatives / Total without Condition
Key Relationships
- TN: True Negatives (correctly identified as negative)
- FP: False Positives (incorrectly identified as positive)
- TN + FP: Total number without the condition
- Range: 0% (no discrimination) to 100% (perfect discrimination)
Remember: High specificity minimizes false positives but may increase false negatives!
Examples and Applications
🩺 Confirmatory Test
Example: COVID-19 PCR test
TN: 990 healthy people test negative
FP: 10 healthy people test positive
Specificity: 990/1000 = 99%
Interpretation: Excellent for confirmation
🔬 Laboratory Test
Example: Pregnancy test
TN: 95 non-pregnant women test negative
FP: 5 non-pregnant women test positive
Specificity: 95/100 = 95%
Interpretation: Very good reliability
🤖 ML Classification
Example: Email spam filter
TN: 900 normal emails classified as normal
FP: 100 normal emails classified as spam
Specificity: 900/1000 = 90%
Interpretation: Good but could be improved