Statistika Non- Parametrik

Authors

  • Ika Dini Mulyani Universitas Pembangunan Panca Budi, Indonesia
  • Nurul Ismy Universitas Pembangunan Panca Budi, Indonesia
  • Hadi Saputra Panggabean Universitas Pembangunan Panca Budi, Indonesia

DOI:

https://doi.org/10.57235/aurelia.v4i2.6752

Keywords:

Non-parametric statistics, data analysis, normal distribution, ordinal data, nominal data, binomial test, chi-square test, Wilcoxon test, research methodology

Abstract

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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References

Djarwanto. 1991. Statsitik Non Parametrik. Edisi 2. Yogyakarta: BPFE

Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: the basic concept and the practical use. Korean Journal of Anesthesiology, 69(1), 8. https://doi.org/10.4097/kjae.2016.69.1.8

Trimawartinah, MKM, Bahan Ajar Statistik Non Parametrik, (2020)

Turner, J. L. (2014). Using Statistics in SmallScale Language Education Research: Focus on Non-Parametric Data. New York and London: Routledge. Retrieved from www.routledge.com/education

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Published

2025-07-31

Issue

Vol. 4 No. 2 (2025): July 2025

Section

Articles

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