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One data set, many analysts: Implications for practicing scientists
Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations in...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971968/ https://www.ncbi.nlm.nih.gov/pubmed/36865366 http://dx.doi.org/10.3389/fpsyg.2023.1094150 |
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author | Kummerfeld, Erich Jones, Galin L. |
author_facet | Kummerfeld, Erich Jones, Galin L. |
author_sort | Kummerfeld, Erich |
collection | PubMed |
description | Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results. The findings demonstrate that different teams analyzing the same data may reach different conclusions. This is the “many-analysts” problem. Previous research on the many-analysts problem focused on demonstrating its existence, without identifying specific practices for solving it. We address this gap by identifying three pitfalls that have contributed to the variability observed in many-analysts publications and providing suggestions on how to avoid them. |
format | Online Article Text |
id | pubmed-9971968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99719682023-03-01 One data set, many analysts: Implications for practicing scientists Kummerfeld, Erich Jones, Galin L. Front Psychol Psychology Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results. The findings demonstrate that different teams analyzing the same data may reach different conclusions. This is the “many-analysts” problem. Previous research on the many-analysts problem focused on demonstrating its existence, without identifying specific practices for solving it. We address this gap by identifying three pitfalls that have contributed to the variability observed in many-analysts publications and providing suggestions on how to avoid them. Frontiers Media S.A. 2023-02-14 /pmc/articles/PMC9971968/ /pubmed/36865366 http://dx.doi.org/10.3389/fpsyg.2023.1094150 Text en Copyright © 2023 Kummerfeld and Jones. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Kummerfeld, Erich Jones, Galin L. One data set, many analysts: Implications for practicing scientists |
title | One data set, many analysts: Implications for practicing scientists |
title_full | One data set, many analysts: Implications for practicing scientists |
title_fullStr | One data set, many analysts: Implications for practicing scientists |
title_full_unstemmed | One data set, many analysts: Implications for practicing scientists |
title_short | One data set, many analysts: Implications for practicing scientists |
title_sort | one data set, many analysts: implications for practicing scientists |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971968/ https://www.ncbi.nlm.nih.gov/pubmed/36865366 http://dx.doi.org/10.3389/fpsyg.2023.1094150 |
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