The probability that [a woman in this cohort] has breast cancer is 0.8 percent. If a woman has breast cancer, the probability is 90 percent that she will have a positive mammogram. If a woman does not have breast cancer, the probability is 7 percent that she will still have a positive mammogram. Imagine a woman who has a positive mammogram, what is the probability that she actually has breast cancer? ...   [When 24 doctors were asked this question], their estimates whipsawed from 1 percent to 90 percent. Eight of them thought the chances were 10 percent or less, 8 more said 90 percent, and the remaining 8 guessed somewhere between 50 and 80 percent. Imagine how upsetting it would be as a patient to hear such divergent opinions.  Imagine 10,000 women were screened.   Construct a contingency table if a total of 10,000 women were screened. What is the correct answer? (Imagine a woman who has a positive mammogram, what is the probability that she actually has breast cancer?)

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The probability that [a woman in this cohort] has breast cancer is 0.8 percent. If a woman has breast cancer, the probability is 90 percent that she will have a positive mammogram. If a woman does not have breast cancer, the probability is 7 percent that she will still have a positive mammogram. Imagine a woman who has a positive mammogram, what is the probability that she actually has breast cancer? ...

 

[When 24 doctors were asked this question], their estimates whipsawed from 1 percent to 90 percent. Eight of them thought the chances were 10 percent or less, 8 more said 90 percent, and the remaining 8 guessed somewhere between 50 and 80 percent. Imagine how upsetting it would be as a patient to hear such divergent opinions.  Imagine 10,000 women were screened.

 

  1. Construct a contingency table if a total of 10,000 women were screened.
  2. What is the correct answer? (Imagine a woman who has a positive mammogram, what is the probability that she actually has breast cancer?)
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