Anew test has been developed to detect a particular type of cancer. the test must be evaluated before it is put into use. a medical researcher selects a random sample of 1,000 adults and finds (by other means) that 4% have this type of cancer. each of the 1,000 adults is given the new test, and it is found that the test indicates cancer in 99% of those who have it and in 1% of those who do not.
a) based on these results, what is the probability of a randomly chosen person having cancer given that the test indicates cancer?
b) what is the probability of a person having cancer given that the test does not indicate cancer?

85.560%

step-by-step explanation:

conditional probability is defined as:

p(a|b) = p(a∩b) / p(b)

in other words, the probability that a occurs, given that b has occurred = the probability that both a and b occur / the probability that b occurs.

in this example:

p(turns 64 | turns 44) = p(turns 64 & turns 44) / p(turns 44).

if someone turns 64, they must have already turned 44, so:

p(turns 64 | turns 44) = p(turns 64) / p(turns 44)

on page 42, we see that of the original 100,000, 94944 turn 44, and 81234 turn 64.   therefore:

p(turns 64 | turns 44) = 81234 / 94944

p(turns 64 | turns 44) = 0.85560

they go with it happens or they mite give a chance?

step-by-step explanation: