Do Not Abandon Statistical Significance

[From the article “The Importance of Predefined Rules and Prespecified Statistical Analyses: Do Not Abandon Significance” by John Ioannidis, published in JAMA]
“A recent proposal to ban statistical significance gained campaign-level momentum in a commentary with 854 recruited signatories. The petition proposes retaining P values but abandoning dichotomous statements (significant/nonsignificant), suggests discussing “compatible” effect sizes, denounces “proofs of the null,” and points out that “crucial effects” are dismissed on discovery or refuted on replication because of nonsignificance.”
“Changing the approach to defining statistical and clinical significance has some merits; for example, embracing uncertainty, avoiding hyped claims with weak statistical support, and recognizing that “statistical significance” is often poorly understood. However…The statistical data analysis is often the only piece of evidence processing that has a chance of being objectively assessed before experts, professional societies, and governmental agencies begin to review the data and make recommendations.”
“The proposal to entirely remove the barrier does not mean that scientists will not often still wish to interpret their results as showing important signals and fit preconceived notions and biases. With the gatekeeper of statistical significance, eager investigators whose analyses yield, for example, P = .09 have to either manipulate their statistics to get to P < .05 or add spin to their interpretation to suggest that results point to an important signal through an observed “trend.” When that gatekeeper is removed, any result may be directly claimed to reflect an important signal or fit to a preexisting narrative.”
“…there is an advantage in having some agreement about default statistical analysis and interpretation. Deviations from the default would then be easier to spot and questioned as to their appropriateness. For most research questions, post hoc analytical manipulation is unlikely to lead closer to the truth than a default analysis with a basic set of rules.”
“Banning statistical significance while retaining P values (or confidence intervals) will not improve numeracy and may foster statistical confusion and create problematic issues with study interpretation, a state of statistical anarchy. Uniformity in statistical rules and processes makes it easier to compare like with like and avoid having some associations and effects be more privileged than others in unwarranted ways. Without clear rules for the analyses, science and policy may rely less on data and evidence and more on subjective opinions and interpretations.”
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