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      Beyond differences in means: robust graphical methods to compare two groups in neuroscience.

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          Abstract

          If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist's toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and Matlab of the graphical tools, and all the examples in the article can be reproduced using R scripts. This article is protected by copyright. All rights reserved.

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          Author and article information

          Journal
          Eur. J. Neurosci.
          The European journal of neuroscience
          Wiley-Blackwell
          1460-9568
          0953-816X
          May 24 2017
          Affiliations
          [1 ] Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, 58 Hillhead Street, G12 8QB, Glasgow, UK.
          [2 ] Centre for Clinical Brain Sciences, Neuroimaging Sciences, University of Edinburgh, Chancellor's Building, Edinburgh, EH16 4SB, UK.
          [3 ] Dept. of Psychology, University of Southern California, Los Angeles, CA 90089-1061, USA.
          Article
          10.1111/ejn.13610
          28544058

          Comments

          Please note that an open copy of this paper can be found here on Figshare: https://figshare.com/articles/Modern_graphical_methods_to_compare_two_groups_of_observations/4055970/6

          2017-06-12 21:04 UTC
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