Abandon Statistical Significance

• Published in: Grantee Submission Vol. 73; no. sup1; pp. 235 - 245
• Main Authors: McShane, Blakeley B., Gal, David, Gelman, Andrew, Robert, Christian, Tackett, Jennifer L.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 29.03.2019; American Statistical Association
• Subjects: Biomedicine; Computer Science; Confidence intervals; Decision making; Modeling and Simulation; Null hypothesis significance testing; p-Value; Regression analysis; Replication; Replication (Evaluation); Scientific Research; Social Sciences; Sociology of science; Statistical analysis; Statistical methods; Statistical Significance; Statistics; Thresholds; Statistical significance; p-Value; Replication; Sociology of science; Null hypothesis significance testing
• ISSN: 0003-1305; 1537-2731
• DOI: 10.1080/00031305.2018.1527253

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm-and the p-value thresholds intrinsic to it-as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to "ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.