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      Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature

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      PLoS Biology
      Public Library of Science

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          Abstract

          We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

          Author summary

          Biomedical science, psychology, and many other fields may be suffering from a serious replication crisis. In order to gain insight into some factors behind this crisis, we have analyzed statistical information extracted from thousands of cognitive neuroscience and psychology research papers. We established that the statistical power to discover existing relationships has not improved during the past half century. A consequence of low statistical power is that research studies are likely to report many false positive findings. Using our large dataset, we estimated the probability that a statistically significant finding is false (called false report probability). With some reasonable assumptions about how often researchers come up with correct hypotheses, we conclude that more than 50% of published findings deemed to be statistically significant are likely to be false. We also observed that cognitive neuroscience studies had higher false report probability than psychology studies, due to smaller sample sizes in cognitive neuroscience. In addition, the higher the impact factors of the journals in which the studies were published, the lower was the statistical power. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

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          Most cited references32

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          Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition.

          Functional magnetic resonance imaging (fMRI) studiesofemotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between brain activation and personality measures. We show that these correlations are higher than should be expected given the (evidently limited) reliability of both fMRI and personality measures. The high correlations are all the more puzzling because method sections rarely contain much detail about how the correlations were obtained. We surveyed authors of 55 articles that reported findings of this kind to determine a few details on how these correlations were computed. More than half acknowledged using a strategy that computes separate correlations for individual voxels and reports means of only those voxels exceeding chosen thresholds. We show how this nonindependent analysis inflates correlations while yielding reassuring-looking scattergrams. This analysis technique was used to obtain the vast majority of the implausibly high correlations in our survey sample. In addition, we argue that, in some cases, other analysis problems likely created entirely spurious correlations. We outline how the data from these studies could be reanalyzed with unbiased methods to provide accurate estimates of the correlations in question and urge authors to perform such reanalyses. The underlying problems described here appear to be common in fMRI research of many kinds-not just in studies of emotion, personality, and social cognition.
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            The Rules of the Game Called Psychological Science.

            If science were a game, a dominant rule would probably be to collect results that are statistically significant. Several reviews of the psychological literature have shown that around 96% of papers involving the use of null hypothesis significance testing report significant outcomes for their main results but that the typical studies are insufficiently powerful for such a track record. We explain this paradox by showing that the use of several small underpowered samples often represents a more efficient research strategy (in terms of finding p < .05) than does the use of one larger (more powerful) sample. Publication bias and the most efficient strategy lead to inflated effects and high rates of false positives, especially when researchers also resorted to questionable research practices, such as adding participants after intermediate testing. We provide simulations that highlight the severity of such biases in meta-analyses. We consider 13 meta-analyses covering 281 primary studies in various fields of psychology and find indications of biases and/or an excess of significant results in seven. These results highlight the need for sufficiently powerful replications and changes in journal policies.
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              Null hypothesis significance testing: a review of an old and continuing controversy.

              Null hypothesis significance testing (NHST) is arguably the most widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is misunderstood by many of those who use it. Several other objections to its use have also been raised. In this article the author reviews and comments on the claimed misunderstandings as well as on other criticisms of the approach, and he notes arguments that have been advanced in support of NHST. Alternatives and supplements to NHST are considered, as are several related recommendations regarding the interpretation of experimental data. The concluding opinion is that NHST is easily misunderstood and misused but that when applied with good judgment it can be an effective aid to the interpretation of experimental data.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                2 March 2017
                March 2017
                2 March 2017
                : 15
                : 3
                : e2000797
                Affiliations
                [1 ]Department of Psychology, University of Cambridge, Cambridge, United Kingdom
                [2 ]Meta-Research Innovation Center at Stanford (METRICS) and Department of Medicine, Department of Health Research and Policy, and Department of Statistics, Stanford University, Stanford, California, United States of America
                University of Amsterdam, Netherlands
                Author notes

                The authors have declared that no competing interests exist.

                Article
                pbio.2000797
                10.1371/journal.pbio.2000797
                5333800
                28253258
                0612dd61-89b4-4a73-ac7e-eab8ba7fc566
                © 2017 Szucs, Ioannidis

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 August 2016
                : 6 February 2017
                Page count
                Figures: 5, Tables: 1, Pages: 18
                Funding
                James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition (grant number 220020370). Received by DS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Meta-Research Article
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Neuroscience
                Biology and Life Sciences
                Neuroscience
                Cognitive Neuroscience
                Biology and Life Sciences
                Psychology
                Social Sciences
                Psychology
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Data
                Biology and Life Sciences
                Neuroscience
                Behavioral Neuroscience
                Physical Sciences
                Mathematics
                Probability Theory
                Statistical Distributions
                Biology and Life Sciences
                Neuroscience
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Life sciences
                Life sciences

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