Cargando…
P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016)
p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observati...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411107/ https://www.ncbi.nlm.nih.gov/pubmed/30856227 http://dx.doi.org/10.1371/journal.pone.0213454 |
_version_ | 1783402339594403840 |
---|---|
author | Simonsohn, Uri Nelson, Leif D. Simmons, Joseph P. |
author_facet | Simonsohn, Uri Nelson, Leif D. Simmons, Joseph P. |
author_sort | Simonsohn, Uri |
collection | PubMed |
description | p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p-curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p-curve. P-curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential. |
format | Online Article Text |
id | pubmed-6411107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64111072019-04-01 P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) Simonsohn, Uri Nelson, Leif D. Simmons, Joseph P. PLoS One Formal Comment p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p-curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p-curve. P-curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential. Public Library of Science 2019-03-11 /pmc/articles/PMC6411107/ /pubmed/30856227 http://dx.doi.org/10.1371/journal.pone.0213454 Text en © 2019 Simonsohn et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Formal Comment Simonsohn, Uri Nelson, Leif D. Simmons, Joseph P. P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title | P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title_full | P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title_fullStr | P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title_full_unstemmed | P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title_short | P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) |
title_sort | p-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: comment on bruns & ioannidis (2016) |
topic | Formal Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411107/ https://www.ncbi.nlm.nih.gov/pubmed/30856227 http://dx.doi.org/10.1371/journal.pone.0213454 |
work_keys_str_mv | AT simonsohnuri pcurvewontdoyourlaundrybutitwilldistinguishreplicablefromnonreplicablefindingsinobservationalresearchcommentonbrunsioannidis2016 AT nelsonleifd pcurvewontdoyourlaundrybutitwilldistinguishreplicablefromnonreplicablefindingsinobservationalresearchcommentonbrunsioannidis2016 AT simmonsjosephp pcurvewontdoyourlaundrybutitwilldistinguishreplicablefromnonreplicablefindingsinobservationalresearchcommentonbrunsioannidis2016 |