The American Statistical Association recently released a special edition of the American Statistician on “Statistical Inference in the 21st Century: A World Beyond p < 0.05” in which the lead editorial calls for abandoning the use of “statistically significant” and offers much to replace it. Doctoral student Lee Kennedy-Shaffer provides his perspective on the debate around p-values and hypothesis testing in an article published in the new issue on “Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing.”
As statisticians and scientists consider a world beyond p < 0.05, it is important to not lose sight of how we got to this point. Although significance testing and p-values are often presented as prescriptive procedures, they came about through a process of refinement and extension to other disciplines. Ronald A. Fisher and his contemporaries formalized these methods in the early twentieth century and Fisher’s 1925 Statistical Methods for Research Workers brought the techniques to experimentalists in a variety of disciplines. Understanding how these methods arose, spread, and were argued over since then illuminates how p < 0.05 came to be a standard for scientific inference, the advantage it offered at the time, and how it was interpreted. This historical perspective can inform the work of statisticians today by encouraging thoughtful consideration of how their work, including proposed alternatives to the p-value, will be perceived and used by scientists. And it can engage students more fully and encourage critical thinking rather than rote applications of formulae. Incorporating history enables students, practitioners, and statisticians to treat the discipline as an ongoing endeavor, crafted by fallible humans, and provides a deeper understanding of the subject and its consequences for science and society. Read full article here.