A Perspective for Strengthening Scholarship in Statistics

Abstract

Everyone closely concerned with the field of statistics is familiar with recurrent discussions about the difficulty and the importance of developing training programs which adequately integrate the theoretical and applied aspects of the analysis and interpretation of scientific research data. It now is increasingly clear that the difficult problems of training in statistics are linked with live substantive problems involving several aspects of theoretical and applied statistics. As statistical concepts and techniques have found broader and deeper roles in various disciplines during recent years, they have also encountered more sophisticated scientific-methodological challenges. These challenges concern the work-a-day techniques of data analysis, and the “elementary” concepts of interpretation of statistical research data, in ways which do matter in typical applications as well as in the theory of statistics. The following represent only a few of the kinds of challenges referred to: 1. Can we give a coherent definition of a best test of a statistical hypothesis, which is compatible with some systematic theory and also represents adequately usual applications? What about : randomized best tests? conditional tests? interpreting power along with significance level in data analysis? subject-matter significance vs. formal statistical significance? fixed-level theory vs. variable-level practice?