Conditional probability is quite helpful for discussing disease testing, particularly for cases where false positives and false negatives are possible. For the following scenario, we'll use events A and B as follows: * Let A be the event that a patient has a disease. * Let B be the event that a patient tests positive for the disease. Assume that we know that about 5% of our population has the disease. Also assume that we know that the probability of a positive test given that the person has the disease is 97%. Finally, assume that the test gives a false positive 4% of the time. P(A) = 3) P(B \, | \, A) = 4) P(B \, | \, \ {not } A) = 5) P(A \ { and } B) = 6) P(\ {not } A \ { and not } B) = 7) P(A \ {and not} B) = 8) P(\text{not } A \text{ and } B) = 9) P(A \, | \, B) =