In many expositions of fuzzy methods, fuzzy techniques are described as
an alternative to a more traditional statistical approach. In this
paper, we present a class of fuzzy statistical decision process in
which testing hypothesis can be naturally reformulated in terms of
interval-valued statistics. We provide the definitions of fuzzy mean,
fuzzy distance as well as investigation of their related properties. We
also give some empirical examples to illustrate the techniques and to
analyze fuzzy data. Empirical studies show that fuzzy hypothesis
testing with soft computing for interval data are more realistic and
reasonable in the social science research. Finally certain comments are
suggested for the further studies. We hope that this reformation will
make the corresponding fuzzy techniques more acceptable to researchers
whose only experience is in using traditional statistical methods.