1. What is Sensitivity?

Sensitivity (also called the true positive rate) measures the proportion of actual positives that are correctly identified by a test. It's a crucial metric for evaluating diagnostic tests.

2. How Does the Calculator Work?

The calculator uses the sensitivity formula:

Where:

• \( TP \) — True Positives (correctly identified positive cases)
• \( FN \) — False Negatives (positive cases incorrectly identified as negative)

Explanation: Sensitivity ranges from 0 to 1, with 1 indicating perfect identification of all positive cases.

3. Importance of Sensitivity Calculation

Details: Sensitivity is particularly important for tests where missing a positive case could have serious consequences (e.g., infectious diseases, cancer screening).

4. Using the Calculator

Tips: Enter the number of true positives and false negatives from your test results. Both values must be non-negative integers, and their sum must be greater than zero.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between sensitivity and specificity?
A: Sensitivity measures how well a test identifies true positives, while specificity measures how well it identifies true negatives.

Q2: What is a good sensitivity value?
A: Generally, values above 0.9 are considered excellent, 0.8-0.9 good, and below 0.7 may be inadequate depending on the clinical context.

Q3: How is sensitivity related to recall?
A: In statistics and machine learning, sensitivity is identical to recall.

Q4: Can sensitivity be 100%?
A: In theory yes, but in practice this usually comes at the cost of reduced specificity (more false positives).

Q5: When is high sensitivity most important?
A: For screening tests or when the cost of missing a case is high (e.g., HIV testing, cancer screening).