Jones, Pete R.;
(2016)
Detecting statistical outliers in psychophysical data.
(BioRxiv
).
Cold Spring Harbor Laboratory
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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 Sn. 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 Sn is included.
| Type: | Working / discussion paper |
|---|---|
| Title: | Detecting statistical outliers in psychophysical data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1101/074591 |
| Publisher version: | https://doi.org/10.1101/074591 |
| Language: | English |
| 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices 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/10055593 |
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