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
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 neglected factors (e.g., prior and related
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 neglected factors. Instead, we offer recommendations
for how our proposal can be implemented in the scientific publication process
as well as in statistical decision making more broadly.
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