One of the most interesting applications of the results of probability theory involves estimating unknown probability and making decisions on the basis of new (sample) information. Biomedical scientists often use the Bayesian decision theory for the purposes of computing diagnostic values such as sensitivity and specificity for a certain diagnostic test and from which positive or negative predictive values are obtained in other to make decisions concerning the well-being of the patient. Often times error rates are encountered and estimated from the results of trials of the screening test with a view to calculating the overall case rate for which an accurate estimate is rarely available. The concept of conditional probability takes into account information about the occurrence of one event to predict the probability of another event. It is on this premise that this article presents Bayes’ theorem as a vital tool. A brief intuitive development of this theorem and its application in diagnosis is given with minimum proof and examples.