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A note on detecting statistical outliers in psychophysical data

Jones, P. R. ORCID: 0000-0001-7672-8397 (2019). A note on detecting statistical outliers in psychophysical data. Attention, Perception, and Psychophysics,


This paper considers how 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 spread known as S n . This is shown to be more sensitive than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on percentiles or interquartile range. MATLAB code for computing S n is included.

Publication Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in 'Attention, Perception & Psychophysics'. The final authenticated version is to be available online at:
Subjects: H Social Sciences > HA Statistics
R Medicine > RE Ophthalmology
Departments: School of Health Sciences > Optometry & Visual Science
[img] Text - Accepted Version
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