Setting an optimal α that minimizes errors in null hypothesis significance tests.

Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which w...

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Autores principales:	Joseph F Mudge, Leanne F Baker, Christopher B Edge, Jeff E Houlahan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa4cfdbd11174377bacc2dd7e5c8c8fd
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spelling	oai:doaj.org-article:aa4cfdbd11174377bacc2dd7e5c8c8fd2021-11-18T07:26:31ZSetting an optimal α that minimizes errors in null hypothesis significance tests.1932-620310.1371/journal.pone.0032734https://doaj.org/article/aa4cfdbd11174377bacc2dd7e5c8c8fd2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22389720/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which we have the highest possible confidence, then the only logical decision-making threshold is the value that minimizes the probability (or occasionally, cost) of making errors. Setting α to minimize the combination of Type I and Type II error at a critical effect size can easily be accomplished for traditional statistical tests by calculating the α associated with the minimum average of α and β at the critical effect size. This technique also has the flexibility to incorporate prior probabilities of null and alternate hypotheses and/or relative costs of Type I and Type II errors, if known. Using an optimal α results in stronger scientific inferences because it estimates and minimizes both Type I errors and relevant Type II errors for a test. It also results in greater transparency concerning assumptions about relevant effect size(s) and the relative costs of Type I and II errors. By contrast, the use of α = 0.05 results in arbitrary decisions about what effect sizes will likely be considered significant, if real, and results in arbitrary amounts of Type II error for meaningful potential effect sizes. We cannot identify a rationale for continuing to arbitrarily use α = 0.05 for null hypothesis significance tests in any field, when it is possible to determine an optimal α.Joseph F MudgeLeanne F BakerChristopher B EdgeJeff E HoulahanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 2, p e32734 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joseph F Mudge Leanne F Baker Christopher B Edge Jeff E Houlahan Setting an optimal α that minimizes errors in null hypothesis significance tests.
description	Null hypothesis significance testing has been under attack in recent years, partly owing to the arbitrary nature of setting α (the decision-making threshold and probability of Type I error) at a constant value, usually 0.05. If the goal of null hypothesis testing is to present conclusions in which we have the highest possible confidence, then the only logical decision-making threshold is the value that minimizes the probability (or occasionally, cost) of making errors. Setting α to minimize the combination of Type I and Type II error at a critical effect size can easily be accomplished for traditional statistical tests by calculating the α associated with the minimum average of α and β at the critical effect size. This technique also has the flexibility to incorporate prior probabilities of null and alternate hypotheses and/or relative costs of Type I and Type II errors, if known. Using an optimal α results in stronger scientific inferences because it estimates and minimizes both Type I errors and relevant Type II errors for a test. It also results in greater transparency concerning assumptions about relevant effect size(s) and the relative costs of Type I and II errors. By contrast, the use of α = 0.05 results in arbitrary decisions about what effect sizes will likely be considered significant, if real, and results in arbitrary amounts of Type II error for meaningful potential effect sizes. We cannot identify a rationale for continuing to arbitrarily use α = 0.05 for null hypothesis significance tests in any field, when it is possible to determine an optimal α.
format	article
author	Joseph F Mudge Leanne F Baker Christopher B Edge Jeff E Houlahan
author_facet	Joseph F Mudge Leanne F Baker Christopher B Edge Jeff E Houlahan
author_sort	Joseph F Mudge
title	Setting an optimal α that minimizes errors in null hypothesis significance tests.
title_short	Setting an optimal α that minimizes errors in null hypothesis significance tests.
title_full	Setting an optimal α that minimizes errors in null hypothesis significance tests.
title_fullStr	Setting an optimal α that minimizes errors in null hypothesis significance tests.
title_full_unstemmed	Setting an optimal α that minimizes errors in null hypothesis significance tests.
title_sort	setting an optimal α that minimizes errors in null hypothesis significance tests.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/aa4cfdbd11174377bacc2dd7e5c8c8fd
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Setting an optimal α that minimizes errors in null hypothesis significance tests.

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