Parametric statistics makes it easier to analyze and describe data with central tendencies and data transformations, according to University of Leicester. The use of parametric statistics versus nonparametric statistics depends on the type of data.
Data for parametric statistics follows a normal distribution, according to Minnesota State University. This means the data points have a mean, or an average, and standard deviation, the standardized dispersion in a data set. Normal data sets have a linearity, which represents a straight line through the data on a graph. Because the data follows a mathematical standardization, researchers make inferences about the data. Parametric statistics is useful for predictions of possible data points not available within the data set. Nonlinear data becomes linear through the use of data transformation in parametric statistics, such as using square roots of the data or creating logarithms. Parametric statistics incorporate data that is on a scale or set as a ratio, which makes mathematical manipulation of the data possible. Due to the requirements to use parametric statistics, researchers who employ these techniques retain more validity within the results.
Correlation, regression, t-tests and analysis of the variance are some of the popular parametric statistical techniques. These tests express the relationship between two or more variables.