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
