Fuzzy sets approaches to statistical parametric and nonparametric tests

Cengiz Kahraman, Cafer Erhan Bozdaǧ*, Da Ruan, Ahmet Fahri Özok

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

40 Citations (Scopus)


The parametric tests often require that the population distributions be normal or approximately so. Statistical methods that do not require the knowledge of the population distribution or its parameters are called nonparametric tests. In this article, first we review some industrial applications of fuzzy parametric tests. Then we present some new algorithms for fuzzy nonparametric tests, namely a fuzzy sign test and a fuzzy Wilcoxon signed-ranks test. Later, we further give fuzzy parametric tests, fuzzy nonparametric tests, and their numerical applications, and also provide a comparison study on crisp and fuzzy nonparametric tests. When the data are vague, the result of the fuzzy nonparametric tests may be different from that of the crisp nonparametric tests.

Original languageEnglish
Pages (from-to)1069-1087
Number of pages19
JournalInternational Journal of Intelligent Systems
Issue number11
Publication statusPublished - Nov 2004


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