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      Low statistical power in biomedical science: a review of three human research domains

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          Abstract

          Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result. We conducted a review of meta-analyses of studies investigating the association of biological, environmental or cognitive parameters with neurological, psychiatric and somatic diseases, excluding treatment studies, in order to estimate the average statistical power across these domains. Taking the effect size indicated by a meta-analysis as the best estimate of the likely true effect size, and assuming a threshold for declaring statistical significance of 5%, we found that approximately 50% of studies have statistical power in the 0–10% or 11–20% range, well below the minimum of 80% that is often considered conventional. Studies with low statistical power appear to be common in the biomedical sciences, at least in the specific subject areas captured by our search strategy. However, we also observe evidence that this depends in part on research methodology, with candidate gene studies showing very low average power and studies using cognitive/behavioural measures showing high average power. This warrants further investigation.

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          Most cited references15

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          Sifting the evidence-what's wrong with significance tests?

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            Current Incentives for Scientists Lead to Underpowered Studies with Erroneous Conclusions

            We can regard the wider incentive structures that operate across science, such as the priority given to novel findings, as an ecosystem within which scientists strive to maximise their fitness (i.e., publication record and career success). Here, we develop an optimality model that predicts the most rational research strategy, in terms of the proportion of research effort spent on seeking novel results rather than on confirmatory studies, and the amount of research effort per exploratory study. We show that, for parameter values derived from the scientific literature, researchers acting to maximise their fitness should spend most of their effort seeking novel results and conduct small studies that have only 10%–40% statistical power. As a result, half of the studies they publish will report erroneous conclusions. Current incentive structures are in conflict with maximising the scientific value of research; we suggest ways that the scientific ecosystem could be improved.
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              A peculiar prevalence of p values just below .05.

              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.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                February 2017
                1 February 2017
                1 February 2017
                : 4
                : 2
                : 160254
                Affiliations
                [1 ]Institute of Neurodegenerative Diseases, CNRS UMR-5293, University of Bordeaux , Bordeaux, France
                [2 ]Centre Emile Durkheim, CNRS UMR-5116, University of Bordeaux , Bordeaux, France
                [3 ]Department of Psychology, University of Bath , Bath, UK
                [4 ] MRC Integrative Epidemiology Unit at the University of Bristol, , Bristol, UK
                [5 ]UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol , Bristol, UK
                Author notes
                Author for correspondence: Marcus R. Munafò e-mail: marcus.munafo@ 123456bristol.ac.uk

                Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3668842.

                Author information
                http://orcid.org/0000-0002-4049-993X
                Article
                rsos160254
                10.1098/rsos.160254
                5367316
                28386409
                4439ba38-acbe-4447-8c41-f34283137e8c
                © 2017 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 12 April 2016
                : 4 January 2017
                Funding
                Funded by: Medical Research Council, http://dx.doi.org/10.13039/501100000265;
                Award ID: MC_UU_12013/6
                Categories
                1001
                133
                87
                Psychology and Cognitive Neuroscience
                Research Article
                Custom metadata
                February, 2017

                statistical power,reproducibility,neurology,psychiatry,somatic disease

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