Biased image cropping and non-independent samples

Any figure in a research article will typically represent only a small portion of the total data gained by a researcher for that experiment, and it is therefore key that the figure accurately reflects what was found overall. Furthermore, if individual observations form clusters with differing mean p...

Descripción completa

Detalles Bibliográficos
Autor principal: Brookfield, John F. Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048658/
https://www.ncbi.nlm.nih.gov/pubmed/27716236
http://dx.doi.org/10.1186/s12915-016-0307-9
_version_ 1782457612605849600
author Brookfield, John F. Y.
author_facet Brookfield, John F. Y.
author_sort Brookfield, John F. Y.
collection PubMed
description Any figure in a research article will typically represent only a small portion of the total data gained by a researcher for that experiment, and it is therefore key that the figure accurately reflects what was found overall. Furthermore, if individual observations form clusters with differing mean properties, those individual observations would not represent independent samples from the populations being compared. In this example, the question of how to fairly represent and treat image data is addressed.
format Online
Article
Text
id pubmed-5048658
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-50486582016-10-11 Biased image cropping and non-independent samples Brookfield, John F. Y. BMC Biol Comment Any figure in a research article will typically represent only a small portion of the total data gained by a researcher for that experiment, and it is therefore key that the figure accurately reflects what was found overall. Furthermore, if individual observations form clusters with differing mean properties, those individual observations would not represent independent samples from the populations being compared. In this example, the question of how to fairly represent and treat image data is addressed. BioMed Central 2016-10-04 /pmc/articles/PMC5048658/ /pubmed/27716236 http://dx.doi.org/10.1186/s12915-016-0307-9 Text en © Brookfield. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Comment
Brookfield, John F. Y.
Biased image cropping and non-independent samples
title Biased image cropping and non-independent samples
title_full Biased image cropping and non-independent samples
title_fullStr Biased image cropping and non-independent samples
title_full_unstemmed Biased image cropping and non-independent samples
title_short Biased image cropping and non-independent samples
title_sort biased image cropping and non-independent samples
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048658/
https://www.ncbi.nlm.nih.gov/pubmed/27716236
http://dx.doi.org/10.1186/s12915-016-0307-9
work_keys_str_mv AT brookfieldjohnfy biasedimagecroppingandnonindependentsamples

Ejemplares similares