title: Rank-order versus mean based statistics for neuroimaging.
abstract: 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.
authors: Department of Communication Sciences and Disorders, University of South Carolina, SC 29208, USA. chris@mricro.com
authors: Chris
authors: C
authors: Rorden
authors: Leonardo
authors: L
authors: Bonilha
authors: Thomas E
authors: TE
authors: Nichols
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004833:Epilepsy, Temporal Lobe / Q000473:pathology; D005189:False Positive Reactions; D006624:Hippocampus / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D018401:Sample Size; D018709:Statistics, Nonparametric