Effect Size as a Complementally Statistical Procedure of Testing Hypotheses

Statistical Testing of Hypotheses

TESTING STATISTICAL HYPOTHESES UNDER FUZZY DATA AND BASED ON A NEW SIGNED DISTANCE

This paper deals with the problem of testing statisticalhypotheses when the available data are fuzzy. In this approach, wefirst obtain a fuzzy test statistic based on fuzzy data, and then,based on a new signed distance between fuzzy numbers, we introducea new decision rule to accept/reject the hypothesis of interest.The proposed approach is investigated for two cases: the casewithout nuisance p...

GroupTest: Multiple Testing Procedure for Grouped Hypotheses

In the modern Big Data analysis, testing multiple hypotheses simultaneously has been an important tool in analyzing data arising from scientific studies, such as genetics, astronomy, social sciences and many others. The hypotheses can often be grouped together according to the nature of the scientific investigations. For instance, genes can be grouped according to gene pathways; the nearby pixe...

Testing Statistical Hypotheses in Fuzzy Environment

In traditional statistics all parameters of the mathematical model and possible observations should be well defined. Sometimes such assumption appears too rigid for the real-life problems, especially while dealing with linguistic data or imprecise requirements. To relax this rigidity fuzzy methods are incorporated into statistics. We review hitherto existing achievements in testing statistical ...

A two-step rejection procedure for testing multiple hypotheses

This paper considers p-value based step-wise rejection procedures for testing multiple hypotheses. The existing procedures have used constants as critical values at all steps. With the intention of incorporating the exact magnitude of the p-values at the earlier steps into the decisions at the later steps, this paper applies a different strategy that the critical values at the later steps are d...