Nonparametric statistical methods and their applications in agriculture

It is known that non-parametric statistical methods were developed as an alternative method to be used in cases where one or more assumptions about a parametric test are not provided. In cases where one or more of the assumptions of the analysis of variance cannot be met, non-parametric test methods should be applied. Agricultural data may not always show normal distribution. In such cases, the data is tried to be normalized with different transformation applications. However, when the prerequisites of parametric tests are not met as a result of the transformation, non-parametric test methods are used. For example, calculating over the median instead of the mean may give more accurate results in data showing a right skewed or left skewed distribution. There is not much literature on the analysis of non-parametric methods with statistical package programs. The aim of this study is to make SAS applications with samples obtained from agricultural areas and to reach a large researcher mass. In the study, research was conducted on how non-parametric tests are used in applications according to data type and their applications in some agricultural areas. All non-parametric statistical methods compiled were applied to agricultural data using the SAS onDemand Academics online program, and the procedures were explained step by step. Keywords: Ansary-Bradley test, Sign test, Wilcoxon Rank-Sum test, Wilcoxon Signed-Rank test