In this talk, we will explore the interpretation of diagnostic tests, focusing on how emergency practice veterinarians can use a Bayesian approach to improve clinical decision-making. Emergency cases often demand rapid yet accurate diagnoses, and understanding the nuances behind test results is critical. Diagnostic tests are not simply positive or negative, but must be interpreted in context. By incorporating pre-test probability and likelihood ratios (which reflect test sensitivity and specificity), veterinarians can more precisely estimate the likelihood of disease after receiving test results. This Bayesian framework helps clinicians assess whether a test result increases or decreases confidence in a diagnosis, allowing for better-informed treatment decisions, particularly in the fast-paced setting of emergency medicine. We will also discuss practical tools, such as nomograms, that simplify this process, and emphasize the importance of integrating clinical judgment with statistical probabilities for better patient outcomes.