How Muriel's Tea Stained Management Research Through Statistical Significance Tests

Ronald Fisher created statistical significance tests to provide an easy method anyone could perform. Their simplicity and general applicability spurred adoption, and they became universal in statistical training, and universal training made these tests universal in social science. Editors and reviewers expected to see statistical significance in every paper. But the method has serious deficiencies. Today's more advanced computational capabilities have created opportunities to address these deficiencies and to use statistical analyses that provide better information. This essay introduces four lessons we have learned during our two-decade effort to inform management scholars about limitations of statistical significance tests. First, methodological change is generational and benefits from a focus on doctoral students. Second, criticizing the status quo is not enough: introducing and teaching alternative approaches is essential. Third, in a publish-or-perish world, change initiatives must address publication. Fourth, to speed up progress, leadership by academic organizations and journal editors is essential.