Parametric testing and nonparametric testing are two types of statistical tests used to analyze data. The main difference between them is that parametric testing assumes that the data follows a specific distribution (usually a normal distribution), while nonparametric testing does not make any assumptions about the distribution of the data.

Parametric tests are more powerful and precise when the data follows a normal distribution. They require a larger sample size and may be less accurate if the data is skewed or has outliers. Examples of parametric tests include t-tests, ANOVA, and regression analysis.

Nonparametric tests are more flexible and robust when the data does not follow a normal distribution. They are less sensitive to outliers and require smaller sample sizes. Examples of nonparametric tests include the Wilcoxon rank-sum test, Kruskal-Wallis test, and Spearman's rank correlation coefficient.

In summary, parametric tests are more appropriate when the data follows a normal distribution and nonparametric tests are more appropriate when the data does not follow a normal distribution.