Testing Hypotheses by Fuzzy Methods: A Comparison with the Classical Approach

  • Rédina BerkachyEmail author
  • Laurent Donzé
Part of the Fuzzy Management Methods book series (FMM)


Testing hypotheses could sometimes benefit from the fuzzy context of data or from the lack of precision in specifying the hypotheses. A fuzzy approach is therefore needed for reflecting the right decision regarding these hypotheses. Different methods of testing hypotheses in a fuzzy environment have already been presented. On the basis of the classical approach, we intend to show how to accomplish a fuzzy test. In particular, we consider that the fuzziness does not only come from data but from the hypotheses as well. We complete our review by explaining how to defuzzify the fuzzy test decision by the signed distance method in order to obtain a crisp decision. The detailed procedures are presented with numerical examples of real data. We thus present the pros and cons of both the fuzzy and classical approaches. We believe that both approaches can be used in specific conditions and contexts, and guidelines for their uses should be identified.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Applied Statistics and Modelling, Department of InformaticsFaculty of Economics and Social Sciences, University of FribourgFribourgSwitzerland

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