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Rousselet GA, Pernet CR, Wilcox RR (2017) Beyond differences in means: robust graphical methods to compare two groups in neuroscience. European Journal of Neuroscience, 46(2):1738-1748    
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T-tests and anovas on means are not robust and of limited sensitivity to differences among distributions. Standard illustrations are not sufficiently detailed. We present powerful tools that provide both detailed illustrations of effects and robust inferences to quantify exactly how distributions differ. We provide R and MATLAB functions, as well as R scripts to reproduce all the figures
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