The purpose of this article is to present the concepts involved in analyzing parametric data. The word parametric, or parameter, relates to the nature of data, i.e., the assumptions about particular data. The primary assumptions are that the data are randomly drawn, that the population is normally distributed and that there is homogeneity among variances. Parametric tests are more stringent than nonparametric tests, and the results tend to be more powerful. Theory concerning hypothesis testing is reviewed, and the distinction is made between the null and alternative hypotheses. The null hypothesis assumes no difference exists between two devices being tested while the alternative assumes a difference. The goal of the statistical test is to accept or reject the null hypothesis. However, it can be difficult to choose the correct statistical test to apply to the data. The most frequently applied statistical tests are the t-test and the analysis of variance (ANOVA). Two types of {.-tests (independent and paired) and the one-way ANOVA are discussed with examples. Since a proliferation of statistical software packages are now available to perform calculations, the reader is encouraged to focus on learning which test to apply rather than on unwieldy mathematical equations. Reading about or conducting statistical tests can be frustrating. Nevertheless, to aid in the growth of O&P research, the authors encourage readers planning to conduct or read research to consider the views presented in this article on parametric testing and those that will be presented in a future article on nonparametric testing.