A peculiar prevalence of p values just below .05
E.J. Masicampo & Daniel Lalande
Quarterly Journal of Experimental Psychology, forthcoming
Abstract: In null hypothesis significance testing (NHST), p values are judged relative to an arbitrary threshold for significance (.05). The present work examined whether that standard influences the distribution of p values reported in the psychology literature. We examined a large subset of papers from three highly regarded journals. Distributions of p were found to be similar across the different journals. Moreover, p values were much more common immediately below .05 than would be expected based on the number of p values occurring in other ranges. This prevalence of p values just below the arbitrary criterion for significance was observed in all three journals. We discuss potential sources of this pattern, including publication bias and researcher degrees of freedom.
The opposite of Max Smart's "missed it by THAT much!" A blog post. All too obvious: you mess with the estimation, specification, and reweighting of the S.E.'s (to "correct" for various imaginary problems) until you that p<0 .05=".05" p="p">
With a nod to Tim Harford, who tweeted on this six weeks ago. Anod also to K Lewis...
E.J. Masicampo & Daniel Lalande
Quarterly Journal of Experimental Psychology, forthcoming
Abstract: In null hypothesis significance testing (NHST), p values are judged relative to an arbitrary threshold for significance (.05). The present work examined whether that standard influences the distribution of p values reported in the psychology literature. We examined a large subset of papers from three highly regarded journals. Distributions of p were found to be similar across the different journals. Moreover, p values were much more common immediately below .05 than would be expected based on the number of p values occurring in other ranges. This prevalence of p values just below the arbitrary criterion for significance was observed in all three journals. We discuss potential sources of this pattern, including publication bias and researcher degrees of freedom.
The opposite of Max Smart's "missed it by THAT much!" A blog post. All too obvious: you mess with the estimation, specification, and reweighting of the S.E.'s (to "correct" for various imaginary problems) until you that p<0 .05=".05" p="p">
With a nod to Tim Harford, who tweeted on this six weeks ago. Anod also to K Lewis...
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