City Research Online

Abstract

This paper considers how best to identify statistical outliers in psychophysical datasets, where the underlying sampling distributions are unknown. Eight methods are described, and each is evaluated using Monte Carlo simulations of a typical psychophysical experiment. The best method is shown to be one based on a measure of absolute-deviation known as S n . This method is shown to be more accurate than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on interquartile range. Matlab code for computing S n is included.

Publication Type:	Monograph (Working Paper)
Additional Information:	The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Subjects:	B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HA Statistics
Departments:	School of Health & Psychological Sciences > Optometry & Visual Sciences

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