In hypotheses testing, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which the hypotheses of interest are imprecise. In this paper, we recall and redefine some concepts about testing fuzzy hypotheses and then we provide a minimax approach to the problem of testing fuzzy hypotheses by using crisp (non-fuzzy) data. We give some illustrative/numerical examples, by which we study the effect of fuzziness by using the power functions of minimax tests.