Biomedical research commonly deals with measured values in humans, laboratory animals, and cells. These collections of measured variables, termed data, are analyzed to achieve scientific objectives. In this review, we present an overview of medical statistics, including its concepts, methods, and tips, for biomedical research applications. We first introduce various concepts of medical statistics, such as true values (that would never be seen in the real world), measured values (actual measurements), bias, and random error. Medical statistics deals with the removal of bias and control of random errors to generate scientific evidence. As a statistical method, data description (or data visualization) is the first step that analysts use to examine data. Data descriptions reveal the patterns, trends, and relationships of the variables. Subsequently, data are analyzed using interval estimation (95% confidence intervals) and statistical hypothesis testing to guide conclusions. Herein, we address the limitations of hypothesis testing and the advantages of using 95% confidence intervals. Moreover, we provide a summary of multiple comparison procedures that allow the simultaneous testing of two or more null hypotheses.