Briefly, Bayes theorem allows for the calculation of the post-test probability of a disease, given a pretest probability of disease, a test result, and the 2 x 2 contingency table of the test. In this context, a "test" result does not have to be a lab test; it can be the presence or absence of any clinical finding as ascertained during the history and physical examination. For example, the presence of chest pain, whether the chest pain is substernal, the result of an exercise stress test, and the troponin result all qualify as clinical findings upon which post-test probabilities can be calculated. Although Bayes theorem can be extended to include continuously valued results, it is most convenient to binarize the test result before calculating the probabilities.

To illustrate the use of Bayes theorem, let's pretend you are a primary care physician and that a 55-year-old patient approaches you and says, "I’m having chest pain." When you hear the words "chest pain," the first life-threatening condition you are concerned about is a myocardial infarction. You can ask the question, "What is the likelihood that this patient is having a myocardial infarction?" In this case, the presence or absence of chest pain is the test (which is positive in this patient), and the presence or absence of myocardial infarction is what we're trying to calculate.