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Accuracy when inferential statistics are used as measurement tools
BACKGROUND: Inferential statistical tests that approximate measurement are called acceptance procedures. The procedure includes type 1 error, falsely rejecting the null hypothesis, and type 2 error, failing to reject the null hypothesis when the alternative should be supported. This approach involve...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845471/ https://www.ncbi.nlm.nih.gov/pubmed/27112752 http://dx.doi.org/10.1186/s13104-016-2045-z |
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author | Bradley, Michael T. Brand, Andrew |
author_facet | Bradley, Michael T. Brand, Andrew |
author_sort | Bradley, Michael T. |
collection | PubMed |
description | BACKGROUND: Inferential statistical tests that approximate measurement are called acceptance procedures. The procedure includes type 1 error, falsely rejecting the null hypothesis, and type 2 error, failing to reject the null hypothesis when the alternative should be supported. This approach involves repeated sampling from a distribution with established parameters such that the probabilities of these errors can be ascertained. With low error probabilities the procedure has the potential to approximate measurement. How close this procedure approximates measurement was examined. FINDINGS: A Monte Carlo procedure set the type 1 error at p = 0.05 and the type 2 error at either p = 0.20 or p = 0.10 for effect size values of d = 0.2, 0.5, and 0.8. The resultant values are approximately 15 and 6.25 % larger than the effect sizes entered into the analysis depending on a type 2 error rate of p < 0.20, or p < 0.10 respectively. CONCLUSIONS: Acceptance procedures approximate values wherein a decision could be made. In a health district a deviation at a particular level could signal a change in health. The approximations could be reasonable in some circumstances, but if more accurate measures are desired a deviation could be reduced by the percentage appropriate for the power. The tradeoff for such a procedure is an increase in type 1 error rate and a decrease in type 2 errors. |
format | Online Article Text |
id | pubmed-4845471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48454712016-04-27 Accuracy when inferential statistics are used as measurement tools Bradley, Michael T. Brand, Andrew BMC Res Notes Short Report BACKGROUND: Inferential statistical tests that approximate measurement are called acceptance procedures. The procedure includes type 1 error, falsely rejecting the null hypothesis, and type 2 error, failing to reject the null hypothesis when the alternative should be supported. This approach involves repeated sampling from a distribution with established parameters such that the probabilities of these errors can be ascertained. With low error probabilities the procedure has the potential to approximate measurement. How close this procedure approximates measurement was examined. FINDINGS: A Monte Carlo procedure set the type 1 error at p = 0.05 and the type 2 error at either p = 0.20 or p = 0.10 for effect size values of d = 0.2, 0.5, and 0.8. The resultant values are approximately 15 and 6.25 % larger than the effect sizes entered into the analysis depending on a type 2 error rate of p < 0.20, or p < 0.10 respectively. CONCLUSIONS: Acceptance procedures approximate values wherein a decision could be made. In a health district a deviation at a particular level could signal a change in health. The approximations could be reasonable in some circumstances, but if more accurate measures are desired a deviation could be reduced by the percentage appropriate for the power. The tradeoff for such a procedure is an increase in type 1 error rate and a decrease in type 2 errors. BioMed Central 2016-04-26 /pmc/articles/PMC4845471/ /pubmed/27112752 http://dx.doi.org/10.1186/s13104-016-2045-z Text en © Bradley and Brand. 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 | Short Report Bradley, Michael T. Brand, Andrew Accuracy when inferential statistics are used as measurement tools |
title | Accuracy when inferential statistics are used as measurement tools |
title_full | Accuracy when inferential statistics are used as measurement tools |
title_fullStr | Accuracy when inferential statistics are used as measurement tools |
title_full_unstemmed | Accuracy when inferential statistics are used as measurement tools |
title_short | Accuracy when inferential statistics are used as measurement tools |
title_sort | accuracy when inferential statistics are used as measurement tools |
topic | Short Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845471/ https://www.ncbi.nlm.nih.gov/pubmed/27112752 http://dx.doi.org/10.1186/s13104-016-2045-z |
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