Fuzzy Post-Hoc Tests and an Application

Abstract

One-Way ANOVA is a method widely used in statistical studies to determine whether there is a significant difference between different group means. Post hoc tests are used to reveal which groups these differences are between. While classical post hoc tests are generally based on the knowledge that the data is definite and specific, post hoc tests based on fuzzy logic can better model uncertain information and situations with uncertain parameters. When the literature is examined, it is seen that although there are many studies on fuzzy one-way ANOVA (Fuzzy One-Way ANOVA), multiple comparisons are not made with fuzzy post hoc tests. In this study, the theoretical structure of fuzzy post hoc tests is established and an example is applied regarding the application of these tests. In the study, these tests developed based on statistics and fuzzy logic theory will contribute to the extension of classical post hoc tests to fuzzy environments by allowing the determination of statistical differences between groups with fuzzy data and will reveal how fuzzy post hoc tests differ when compared to traditional methods.