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

Eur J Neurosci. 2017 Jul;46(2):1738-1748. doi: 10.1111/ejn.13610. Epub 2017 Jun 29.


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.

Keywords: data visualisation; difference asymmetry function; quantile estimation; robust statistics; shift function.

MeSH terms

  • Animals
  • Computer Graphics
  • Data Interpretation, Statistical*
  • Guinea Pigs
  • Humans
  • Mycobacterium Infections / mortality
  • Neurosciences / methods*
  • Software
  • Time Factors