Disengaging from statistical significance
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In the marketplace of scientific results, the preferred currency by which results have been valued has been statistical significance, expressed either as a dichotomous label or by the underlying p value, which may be given as a number or an inequality. Like other modern currencies, the value of this one is not inherent but derived from widely held assumptions and expectations. Indeed, reliance on statistical significance is as misplaced as faith in some dubious paper monies. At the risk of stretching the analogy, I suggest that a version of Gresham’s Law has been operating, allowing statistical significance to force out of circulation better ways to analyze data, and leaving us with results that are, all too often, astonishingly misleading.
As the recently published ASA statement [1, 2] indicates, a fundamental flaw of relying on statistical significance for inference is the need to dichotomize all results into those that are significant or not significant. This practice degrades vast...
KeywordsEstimate Effect Size Statistical Significance Testing Measure Effect Size Fruitful Research False Dichotomy
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