RT Journal Article
SR Electronic
T1 Beyond differences in means: robust graphical methods to compare two groups in neuroscience
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 121079
DO 10.1101/121079
A1 Guillaume A. Rousselet
A1 Cyril R. Pernet
A1 Rand R. Wilcox
YR 2017
UL http://biorxiv.org/content/early/2017/03/27/121079.abstract
AB 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. If neuroscience experiments often lead to large, complex and multidimensional data, most analyses rely on t-tests and ANOVAs on means. These analyses are of limited sensitivity to differences among distributions and perform poorly for data that do not conform to the tests’ assumptions. Results also rarely show the underlying distributions by using inappropriate graphical representations such as bar graphs. Here we present powerful alternative tools that provide both detailed illustrations of effects and robust inferences. These tools quantify how distributions differ and can be applied at different levels of analysis. We describe this graphical approach to group comparisons, and provide implementations in R and Matlab.