Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires

By S. Hurlbert, Richard A. Levine, J. Utts. American Statistician, 2019

144 citations recorded by the source corpus.

ABSTRACT Many controversies in statistics are due primarily or solely to poor quality control in journals, bad statistical textbooks, bad teaching, unclear writing, and lack of knowledge of the historical literature. One way to improve the practice of statistics and resolve these issues is to do what initiators of the 2016 ASA statement did: take one issue at a time, have extensive discussions about the issue among statisticians of diverse backgrounds and perspectives and eventually develop and publish a broadly supported consensus on that issue. Upon completion of this task, we then move on to deal with another core issue in the same way. We propose as the next project a process that might lead quickly to a strong consensus that the term “statistically significant” and all its cognates and symbolic adjuncts be disallowed in the scientific literature except where focus is on the history of statistics and its philosophies and methodologies. Calculation and presentation of accurate p-values will often remain highly desirable though not obligatory. Supplementary materials for this article are available online in the form of an appendix listing the names and institutions of 48 other statisticians and scientists who endorse the principal propositions put forward here.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ca0536a853f5dc00998eeb82a08f79aa7e92791b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Moving to a World Beyond “p < 0.05” (2019, unknown): “Ioannidis (2019) notes the high level of selection bias prevalent in biomedical journals.”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “The use of phrases such as “significant” or “nonsignificant” based on dichotomized p values are now strongly discouraged (Hurlbert et al., 2019; Wasserstein et al., 2019).”