<![CDATA[Non-parametric statistics is an essential tool in data analysis, particularly when the assumption of normal distribution cannot be met. This method offers a flexible approach applicable to various types of data, including ordinal and nominal data. This article explores the fundamental principles, methodologies, and challenges of using non-parametric statistics, highlighting advantages such as more lenient assumptions and ease of calculation. Despite its limitations, especially regarding the testing of parametric assumptions and large sample sizes, non-parametric statistics remain a relevant choice. Guidelines for the use of one-sample, two-sample, and more than two-sample tests are presented, along with practical examples such as the binomial test, chi-square test, and Wilcoxon test. With a deep understanding of this method, researchers and practitioners are expected to make better decisions based on valid and reliable data analyses.]]>
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