Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency--for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects.

Original publication




Journal article



Publication Date





1531 - 1537


Algorithms, Brain, Computer Simulation, Data Interpretation, Statistical, Epilepsy, Temporal Lobe, False Positive Reactions, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Sample Size, Statistics, Nonparametric