In most scientific disciplines, the \(p\) -value is, still too often, revered as “the” conclusive criterion for hypothesis testing. For years, scholars from diverse horizons have alerted to the hazards of daredevil claims of “proofs” entailed by mere benchmarking of \(p\) against an arbitrarily predetermined risk tolerance \(\alpha\) -cutoff. Deciding whether empirical results are “statistically significant” or not, through such a dichotomization, not only bypasses the uncertainty hanging over the supposed effects, but also prompts insignificance verdicts. First, a cross-examination of an article published by JMR in 2021 traces the root causes of quick-and-dirty handling of \(p\) , and recaptures the true meaning of this misconstrued statistic. Next, a recalibration of it is advertised, for it yields a measure of the probability that the effect studied is real: its plausibility. Complementarily, to highlight the substantiality of expected effects, forest charts of their compatibility/confidence intervals are promoted. An example borrowed from an article issued by JAMS in 2022 proves the relevance of the add-ons proposed. Finally, the decision aid Bayesian econometrics provide—materialized by curve plots of credible intervals, chances of positive outcomes, and risks of negative ones—is illustrated through a thorough revisit of a 2018 marketing research case.
