The statistical concept of Bayes comes up in clinical medicine all the time. It simply means that what you know about something factors into how you analyze it. This contrasts with the commonly used statistical approach called frequentist analysis of hypothesis testing, in which it is assumed that every situation is unique and not influenced by the past. Bayesian analysis accounts for how prior information gets factored into decision making and is important to understand when applying clinical research findings to the delivery of medical care. In this interview Anna E. McGlothlin, PhD, senior statistical scientist at Berry Consultants in Austin, Texas, explains these concepts for clinicians. Read the article: Bayesian Hierarchical Models