Parametric and Nonparametric Tests in Spine Research: Why Do They Matter?

Parametric and nonparametric are 2 broad classifications of statistical procedures. Parametric tests are based on assumptions about the distribution of the underlying population from which the sample was taken. The most common parametric assumption is that data is approximately normally distributed. Nonparametric tests do not rely on assumptions about the shape or parameters of the underlying population distribution. Use nonparametric tests for categorical data or continuous data that is not normally distributed. The decision on which statistical test to select for analysis can be aided by using a decision tree such as the one given in the text above. Rethink how you approach evaluating a study in the future. Consider looking at the data first and reviewing its characteristics, the shape and the type of data gathered. And then see what the authors chose to do with their data. Assessing research in this manner will assist in determining the validity of the investigators’ methods and subsequent results reporting.