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.
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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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