A peculiar prevalence of <i>p</i> values just below .05

Masicampo, E. J.; Lalande, Daniel

doi:10.1080/17470218.2012.711335

Cited by 253 publications

(271 citation statements)

References 30 publications

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“…However, the presence of an arbitrary threshold may contribute to the number of positive findings reported in the literature. Masicampo and Lalande (2012) surveyed the distributions of p-values in three prominent psychological journals and found that p-values were conspicuously more common than expected just below α.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach to mitigation of publication bias

Guan

Vandekerckhove

2015

Psychon Bull Rev

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The reliability of published research findings in psychology has been a topic of rising concern. Publication bias, or treating positive findings differently from negative findings, is a contributing factor to this "crisis of confidence," in that it likely inflates the number of false positive effects in the literature. We demonstrate a Bayesian model averaging approach that takes into account the possibility of publication bias and allows for a better estimate of true underlying effect size. Accounting for the possibility of bias leads to a more conservative interpretation of published studies as well as metaanalyses. We provide mathematical details of the method and examples.

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Section: Introductionmentioning

confidence: 99%

A Bayesian approach to mitigation of publication bias

Guan

Vandekerckhove

2015

Psychon Bull Rev

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“…Using what are called caliper tests, more comprehensive analyses of p-values in sociology have shown a striking difference in the percentages of z-scores that imply p-values slightly below 0.05 compared to those slightly above 0.05 Auspurg and Hinz 2011). Such findings are not at all unique to sociology (e.g., Gerber and Malhotra [2008b] for political science; Masicampo and Lalande [2012] for psychology), and they provide definitive indication of a specifically p-oriented publication bias in the scientific literature of many fields (Dwan et al 2008).…”

Section: The Cost Of Concisionmentioning

confidence: 82%

Defending the Decimals: How Foolishly False Precision Might Strengthen Social Science

Freese¹

2014

SocScience

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Social scientists often report regression coefficients using more significant figures than are meaningful given measurement precision and sample size. Common sense says we should not do this. Yet, as normative practice, eliminating these extra digits introduces a more serious scientific problem when accompanied by other ascendant reporting practices intended to reduce social science's long-standing emphasis on null hypothesis significance testing. Coefficient p-values can no longer be recovered to the degree of precision that p-values have been abundantly demonstrated to influence actual research practice. Developing methods for detecting and addressing systematically exaggerated effect sizes across collections of studies cannot be done effectively if p-values are hidden. Regarding what is preferable for scientific literature versus an individual study, the costs of false precision are therefore innocuous compared to alternatives that either encourage the continuation of practices known to exaggerate causal effects or thwart assessment of how much such exaggeration occurs.

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“…In studies with true nonsignificant results, the distribution of p-value will have a left skew. 1,5 The two-tailed sign test is used in combination with the P-curve analysis to identify possible P-hacking. It consists of two p-value ranges, 0<p < 0.025 and 0.025 < p < 0.05.…”

Section: Interpreting the P-curvementioning

confidence: 99%