A TRAPEZOIDAL RANKING APPROACH FOR FUZZY HYPOTHESIS TESTING

Fuzzy hypothesis testing is a useful framework for addressing statistical inference problems involving vague or imprecise data. In this paper, we enhance the interpretability and consistency of such tests by introducing a ranking-based approach for comparing trapezoidal fuzzy numbers. The method integrates a geometric and information-sensitive order relation into the defuzzification stage of fuzzy p-values, improving the decision-making process under uncertainty. This modification is applied within an existing fuzzy hypothesis testing structure, where fuzzy null and alternative hypotheses are evaluated, and the resulting p-values are interpreted through ranking rather than arbitrary thresholds. Two numerical examples are provided to illustrate how the proposed ranking step affects the final decision, showing that different defuzzification strategies can lead to distinct conclusions. The proposed approach maintains mathematical rigor while offering a more intuitive interpretation of fuzzy test outcomes. The findings suggest that selecting a robust ranking mechanism is crucial when testing hypotheses based on fuzzy data, and they open new perspectives for applying fuzzy statistics in fields such as engineering, medicine, and social sciences.