Abstract: The aim of this project is to develop a comprehensive database ("Bayes Library") to provide rapid access to data on frequently used diagnostic and screening tests, including signs and symptoms, laboratory tests, x-rays etc. Relevant data on pretest probabilities, test characteristics (sensitivity, specificity, likelihood ratios) and study designs will be identified in comprehensive literature searches, critically appraised, and presented in a standardised format to inform the choice of tests and interpretation of test results. Thomas Bayes (1702-1761, see picture) showed that the post-test probability of a single or a series of tests can be calculated using pretest probability and likelihood ratios, allowing clinicians and consumers to weigh complex information according to its diagnostic importance. [Tables excluded] 4. Example: Thomas, an 18-year old student suffers from moderate (1) right lower abdominal pain with (2) no similar previous episodes. The pain was initially generalised, but has now (3) migrated to the right lower quadrant. Examination shows (4) moderate guarding, and (5) the leukocyte count is 13 x 10-9/L. The combined power of this sequence of tests (1-5) is calculated by multiplying their LRs: 7.3 x 1.5 x 3.2 x 1.6 x 2.4 = 135. Assuming a pretest probability of 1%, a post-test probability of 60% is obtained from a Fagan nomogram. Given potential biases, 40%-50% is a more realistic estimate of the post-test probability of appendicitis Preparing, maintaining and promoting the accessibility of critically appraised diagnostic information will require an international collaborative effort, similar to the Cochrane Collaboration. The proposed Bayes Collaboration and Bayes Library has considerable potential in promoting evidence-based health care.