Remember the recent call to abandon statistical significance in science research? There’s been some pushback:
Statistical significance sets a convenient obstacle to unfounded claims. In my view, removing the obstacle (V. Amrhein et al. Nature 567, 305–307; 2019) could promote bias. Irrefutable nonsense would rule.John P. A. Ioannidis, “Retiring statistical significance would give bias a free pass” at Nature
Ioannidis is a well-known scourge of bad data. See, for example, Another Well-Earned Jab At “Nutrition Science”
A statistician argues that two separate problems Are being conflated:
By focusing on the term ‘statistical significance’, we ignore the more important issue of what constitutes sufficient evidence of a true association. Let’s have that discussion and redefine what we mean by a statistically significant finding. Valen E. Johnson, “Raise the bar rather than retire significance” at Nature
Without the restraint provided by testing, an estimation-only approach will lead to overfitting of research results, poor predictions and overconfident claims. Julia M. Haaf, Alexander Ly & Eric-Jan Wagenmakers, “Retire significance, but still test hypotheses” at Nature
Of course, as science embraces post-modernism, “irrefutable nonsense” could be the new standard. Along with ever more strenuous demands that we trust science.
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See also: Abandon statistical significance, learn to live with uncertainty, scientists demand Let’s see where this goes. Will it lead to less magic with numbers or more and bigger magic?