# 75 5 b since we know the woman is not pregnant we are

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75 5 . b) Since we know the woman is not pregnant, we are limited to the 19 women in the second row, of which 5 had a positive test. P (positive test result | not pregnant) = 263 . 0 19 5 The second result is what is usually called a false positive: A positive result when the woman is not actually pregnant. Positive test Negative test Total Pregnant 70 4 74 Not Pregnant 5 14 19 Total 75 18 93
290 Bayes Theorem In this section we concentrate on the more complex conditional probability problems we began looking at in the last section. Example 19 Suppose a certain disease has an incidence rate of 0.1% (that is, it afflicts 0.1% of the population). A test has been devised to detect this disease. The test does not produce false negatives (that is, anyone who has the disease will test positive for it), but the false positive rate is 5% (that is, about 5% of people who take the test will test positive, even though they do not have the disease). Suppose a randomly selected person takes the test and tests positive. What is the probability that this person actually has the disease? There are two ways to approach the solution to this problem. One involves an important result in probability theory called Bayes' theorem. We will discuss this theorem a bit later, but for now we will use an alternative and, we hope, much more intuitive approach. Let's break down the information in the problem piece by piece. Suppose a certain disease has an incidence rate of 0.1% (that is, it afflicts 0.1% of the population). The percentage 0.1% can be converted to a decimal number by moving the decimal place two places to the left, to get 0.001. In turn, 0.001 can be rewritten as a fraction: 1/1000. This tells us that about 1 in every 1000 people has the disease. (If we wanted we could write P (disease)=0.001.) A test has been devised to detect this disease. The test does not produce false negatives (that is, anyone who has the disease will test positive for it). This part is fairly straightforward: everyone who has the disease will test positive, or alternatively everyone who tests negative does not have the disease. (We could also say P (positive | disease)=1.) The false positive rate is 5% (that is, about 5% of people who take the test will test positive, even though they do not have the disease). This is even more straightforward. Another way of looking at it is that of every 100 people who are tested and do not have the disease, 5 will test positive even though they do not have the disease. (We could also say that P (positive | no disease)=0.05.) Suppose a randomly selected person takes the test and tests positive. What is the probability that this person actually has the disease? Here we want to compute P (disease|positive). We already know that P (positive|disease)=1, but remember that conditional probabilities are not equal if the conditions are switched. Rather than thinking in terms of all these probabilities we have developed, let's create a hypothetical situation and apply the facts as set out above. First, suppose we randomly select 1000 people and administer the test. How many do we expect to have the disease? Since about 1/1000 of all people are afflicted with the disease, 1/1000 of 1000 people is 1. (Now you know why we chose 1000.) Only 1 of 1000 test subjects actually has the disease; the other 999 do not.
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