Decision errors in medical diagnostic testing Consider medical diagnostic testing, such as using a mammogram to detect if a woman may have breast cancer. Define the null hypothesis of no effect as the patient does not have the disease. Define rejecting _H0 as concluding that the patient has the disease. See the table for a summary of the possible outcomes:

a. When a radiologist interprets a mammogram, explain why a Type I error is a false positive, predicting that a woman has breast cancer when actually she does not.

b. A Type II error is a false negative. What does this mean, and what is the consequence of such an error to the woman?

c. A radiologist wants to decrease the chance of telling a woman that she may have breast cancer when actually she does not. Consequently, a positive test result will be reported only when there is extremely strong evidence that breast cancer is present. What is the disadvantage of this approach?