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A note on detecting statistical outliers in psychophysical data

Jones, P; (2019) A note on detecting statistical outliers in psychophysical data. Attention, Perception, and Psychophysics , 81 pp. 1189-1196. 10.3758/s13414-019-01726-3. Green open access

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Abstract

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.

Type: Article
Title: A note on detecting statistical outliers in psychophysical data
Open access status: An open access version is available from UCL Discovery
DOI: 10.3758/s13414-019-01726-3
Publisher version: https://doi.org/10.3758/s13414-019-01726-3
Language: English
Additional information: © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Statistics, Cognitive neuroscience
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10072317
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