An approach to fuzzy hypothesis testing with intuitionistic fuzzy data

Intuitionistic fuzzy theory has been developed and studied over the years in terms of applicability to real world problems. This work will focus on the implementation of hypothesis tests of this theory. The Bootstrap method, which is to obtain a new sample from the existing sample, will be explained in order to provide results closer to the truth. As is well known intuitionistic fuzzy numbers may not be used in systems in the real world. So with the defuzzification of fuzzy number must be provided to be used in the systems. Defuzzification can be summerized as obtaining a crisp number which can represent fuzzy number. Although there are many defuzzification methods, in this work will be used to WABL (Weighted Averaging Based on the Levels) method to recognise the right to speak to decision maker. At the same time, Yao-Wu method which the most commonly used method in the literatüre and the proposed method of study WABL method will be compared and the advantages of the WABL method will be mentioned. In this work, first information about development of fuzzy theory and intuitionistic fuzzy theory are given. Then, the methods of defuzzification has been mentioned and WABL method which is a defuzzification method has been mentioned adaptation of the method to intuitionistic fuzzy numbers. Steps of intuitionistic fuzzy hypothesis testing and Bootstrap method have been mentioned. Latest, all the information that are described theoretically is illustrated on an application. Because these operations are not performed manually, this operations codded in Python programming language.