Answer:
The correct formula for sensitivity is a) TP/(TP+FN) × 100.
Step-by-step explanation:
Sensitivity, also known as the true positive rate or recall, is a measure of how well a test can correctly identify positive cases out of all the actual positive cases. It is commonly used in the field of medical diagnostics and machine learning.
Let's break down the formula step by step:
- TP refers to true positives, which are the cases that are correctly identified as positive by the test.
- FN refers to false negatives, which are the cases that are incorrectly identified as negative by the test, but are actually positive.
To calculate sensitivity, we divide the number of true positives by the sum of true positives and false negatives, and then multiply the result by 100 to express it as a percentage. This formula gives us the proportion of true positives correctly identified by the test.
For example, if we have a total of 100 positive cases and the test correctly identifies 80 of them, while incorrectly classifying 20 as negative, we would have:
- TP = 80
- FN = 20
Plugging these values into the formula, we get:
Sensitivity = 80/(80+20) × 100 = 80%
Therefore, the sensitivity of the test in this case is 80%.
The other options (b), c), and d)) are not the correct formulas for sensitivity. It is important to use the correct formula to ensure accurate calculations and interpretations of test performance.