What is the difference between parametric testing and nonparametric testing？

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.