Abstract
Type I and Type II errors are inherent in any empirical medical research on an antecedent-outcome relationship when it is based on a dataset of a sample of subjects. Type I error is the incorrect rejection of a true null hypothesis, and its probability in a study is the P value. This error is more serious and is kept under control by specifying a cap called the level of significance. The complement of the probability of Type II error, called power, is the probability of not missing a medically significant effect when present. This article concisely explains P values, power, and medical significance in nontechnical terms for our medical colleagues and their implications for assessing the credibility of medical research.