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

This article focuses on the historical development, widespread adoption, and limitations of null hypothesis significance tests (NHSTs), created by Ronald Fisher to simplify statistical analysis in social sciences. While NHSTs became universally taught and expected in research publications, they have significant deficiencies, including misinterpretation, reliance on unrealistic null hypotheses, disregard for prior research, and oversimplification of complex phenomena. The authors discuss four alternative approaches to improve empirical data analysis beyond NHSTs: effect sizes, null models, baseline models, and Bayesian analyses, highlighting their advantages and challenges. They emphasize that methodological change is generational, requires teaching alternatives alongside critiques, and depends on leadership from academic institutions and journal editors to promote adoption.