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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

DOI

10.1016/j.neuroimage.2006.12.043

Type

Journal article

Journal

Neuroimage

Publication Date

01/05/2007

Volume

35

Pages

1531 - 1537

Keywords

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