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Beyond differences in means: robust graphical methods to compare two groups in neuroscience
Author(s) -
Rousselet Guillaume A.,
Pernet Cyril R.,
Wilcox Rand R.
Publication year - 2017
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.13610
Subject(s) - toolbox , complement (music) , computer science , computational neuroscience , scripting language , bar chart , graphical model , function (biology) , artificial intelligence , data science , machine learning , mathematics , programming language , statistics , biology , biochemistry , complementation , evolutionary biology , gene , phenotype
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.