The correct answer is: D. All of the mentioned
Sensitivity, specificity, and median absolute deviation are all common error measures.
Sensitivity is the proportion of people who have the disease who are correctly identified as having the disease by a test. Specificity is the proportion of people who do not have the disease who are correctly identified as not having the disease by a test. Median absolute deviation is a measure of the spread of data around the median.
All three of these measures can be used to evaluate the performance of a test. Sensitivity is important because it tells us how likely the test is to identify people who have the disease. Specificity is important because it tells us how likely the test is to correctly identify people who do not have the disease. Median absolute deviation is important because it tells us how much variation there is in the test results.
In general, we want a test to have high sensitivity and specificity. However, there is often a trade-off between these two measures. For example, a test that is very sensitive may be less specific, and vice versa. The choice of which measure to use depends on the specific application.
For example, if we are developing a test for a rare disease, we may be more willing to accept a test with lower specificity in order to achieve a higher sensitivity. This is because we want to make sure that we do not miss any cases of the disease. On the other hand, if we are developing a test for a common disease, we may be more willing to accept a test with lower sensitivity in order to achieve a higher specificity. This is because we want to make sure that we do not incorrectly diagnose people with the disease.