1. What Are Sensitivity and Specificity?

Sensitivity (true positive rate) measures the proportion of actual positives correctly identified. Specificity (true negative rate) measures the proportion of actual negatives correctly identified. These metrics are fundamental in evaluating binary classification tests.

2. How the Calculator Works

The calculator uses the standard formulas:

Where:

• \( TP \) — True Positives (array)
• \( FN \) — False Negatives (array)
• \( TN \) — True Negatives (array)
• \( FP \) — False Positives (array)

3. Importance in Statistical Analysis

Details: Sensitivity and specificity are crucial for evaluating diagnostic tests, machine learning models, and statistical classifiers across medicine, data science, and research.

4. Using the Calculator

Tips: Enter arrays of binary values (0s and 1s) in Matlab format (e.g., [1,0,1]). The calculator sums the arrays and applies the formulas to compute sensitivity and specificity.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between sensitivity and precision?
A: Sensitivity focuses on false negatives (TP/(TP+FN)), while precision focuses on false positives (TP/(TP+FP)).

Q2: Can sensitivity be 100%?
A: Yes, if there are no false negatives, but this often comes at the cost of lower specificity.

Q3: How are these used in ROC curves?
A: ROC curves plot sensitivity vs (1-specificity) across different classification thresholds.

Q4: What's a good balance between sensitivity and specificity?
A: Depends on context. Medical screening may prioritize sensitivity, while confirmatory tests prioritize specificity.

Q5: How does this relate to F1 score?
A: F1 score is the harmonic mean of precision and sensitivity (recall), while specificity measures performance on negative cases.