# Question

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?

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?

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