Whether data are collected as part of a research study or to help the physician characterize a group of patients, the resulting data fall into either one of two categories: dis­crete or continuous. If the outcome of interest includes values that fall into discrete categories (e.g., gender, race, or presence or absence of disease), statistical tests for discrete data are used. (Descriptive measures and a re­view of statistical tests for these measures were discussed in a previous "Current Concepts" article.) Discrete data are measured in frequencies of occurrence. For example, in a study of injuries among high school athletes, 76 women experienced an injury compared with 24 men. Gender is an example of a discrete variable. In contrast, a continuous variable results in data that can be "charac­terized by having an infinite number of evenly spaced potential values between any two values."For example, weight is an example of a continuous variable. That is, between 180 and 181 pounds there are an unlimited num­ber of possible units of weight that might be measured in infinitely small increments. Other examples of continuous variables include blood pressure, temperature, and height.