Abandon Statistical Significance

TL;DR

The paper advocates for abandoning the traditional null hypothesis significance testing paradigm, including p-value thresholds, in favor of a more nuanced, evidence-based approach that considers multiple factors in scientific research and publication.

Contribution

It proposes replacing NHST with a continuous, evidence-based framework that de-emphasizes p-value thresholds and incorporates various contextual factors in scientific decision-making.

Findings

Critiques the limitations of NHST and p-value thresholds.

Recommends a shift to a multifactor evidence evaluation approach.

Provides implementation suggestions for scientific publication and decision-making.

Abstract

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.