1. What Are Specificity and Sensitivity?

Specificity and sensitivity are statistical measures of the performance of a binary classification test. Sensitivity measures the proportion of actual positives correctly identified, while specificity measures the proportion of actual negatives correctly identified.

2. How Does the Calculator Work?

The calculator uses these formulas:

Where:

• \( TN \) — True negatives (correctly identified negatives)
• \( FP \) — False positives (incorrectly identified positives)
• \( TP \) — True positives (correctly identified positives)
• \( FN \) — False negatives (incorrectly identified negatives)

Explanation: These metrics help evaluate how well a test distinguishes between positive and negative cases.

3. Importance of These Metrics

Details: Sensitivity is important when the cost of missing a positive case is high (e.g., disease screening). Specificity is important when the cost of false alarms is high (e.g., confirmatory testing).

4. Using the Calculator

Tips: Enter the counts from your confusion matrix (TN, FP, TP, FN). All values must be non-negative integers.

5. Frequently Asked Questions (FAQ)

Q1: What is a good sensitivity/specificity value?
A: Generally, values above 80% are considered good, but this depends on the context. Perfect tests would have 100% for both.

Q2: Can sensitivity and specificity be high simultaneously?
A: It's challenging - increasing one often decreases the other. The balance depends on the test's purpose.

Q3: What's the relationship with PPV and NPV?
A: Positive/Negative Predictive Values also depend on disease prevalence, while sensitivity/specificity don't.

Q4: How do these relate to ROC curves?
A: ROC curves plot sensitivity vs (1-specificity) across different test thresholds.

Q5: What if my test has continuous results?
A: You'll need to choose a cutoff threshold to calculate these binary classification metrics.