A novel approach is proposed for the problem of testing statistical hypotheses about the fuzzy mean of a fuzzy random variable.The concept of the (uniformly) most powerful test is extended to the (uniformly) most powerful fuzzy-valued test in which the test function is a fuzzy set representing the degrees of rejection and acceptance of the hypothesis of interest.For this purpose, the concepts o...

There are many situations for statistical process in which we have both random and vagueinformation. When uncertainty is due to fuzziness of information, fuzzy statistical control charts play animportant role in the monitoring process, because they simultaneously deal with both kinds of uncertainty.Dealing with fuzzy characteristics using classical methods may cause the loss of information and ...

For many scientific experiments computing a p-value is the standard method for reporting the outcome. It is a simple way of summarizing the information in the data. One theoretical justification for p-values is the Neyman-Pearson theory of hypotheses testing. However the decision making focus of this theory does not correspond well with the desire, in most scientific experiments, for a simple a...

agility has an undeniable effect on organization success inthe present changing markets, and information technology (it) is a majorfacilitating factor in business activities of organizations nowadays,as about more than a half of present organizations capitals is based onit. therefore, assessment of the it application level and organizationalagility, and study of the effect of application of it ...

Classical statistics and many data mining methods rely on “statistical significance” as a sole criterion for evaluating alternative hypotheses. In this paper, we use a novel, fuzzy logic approach to perform hypothesis testing. The method involves four major steps: hypothesis formulation, data selection (sampling), hypothesis testing (data mining), and decision (results). In the hypothesis formu...