Choosing the Level of Significance: A Decision‐theoretic Approach.
In many areas of science, including business disciplines, statistical decisions are often made almost exclusively at a conventional level of significance. Serious concerns have been raised that this contributes to a range of poor practices such as p‐hacking and data‐mining that undermine research cr...
| Publicado en: | Abacus Vol. 57; no. 1; pp. 27 - 72 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=149411655&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 149411655 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00013072 AUB jtl: Abacus issn: 00013072 maglogo: Y pubinfo: dt: Mar2021 vid: 57 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 149411655 10.1111/abac.12172 ppf: 27 ppct: 45 formats: fmt: @attributes: type: P size: 846KB tig: atl: Choosing the Level of Significance: A Decision‐theoretic Approach. aug: au: Kim, Jae H. Choi, In affil: the Department of Economics and Finance, La Trobe University the Department of Economics, Sogang University su: Statistical decision making False positive error Regression analysis Decision making sug: subj: Statistical decision making False positive error Regression analysis Decision making keyword: Bootstrapping Expected loss Optimal significance level Power analysis Statistical significance ab: In many areas of science, including business disciplines, statistical decisions are often made almost exclusively at a conventional level of significance. Serious concerns have been raised that this contributes to a range of poor practices such as p‐hacking and data‐mining that undermine research credibility. In this paper, we present a decision‐theoretic approach to choosing the optimal level of significance, with a consideration of the key factors of hypothesis testing, including sample size, prior belief, and losses from Type I and II errors. We present the method in the context of testing for linear restrictions in the linear regression model. From the empirical applications in accounting, economics, and finance, we find that the decisions made at the optimal significance levels are more sensible and unambiguous than those at a conventional level, providing inferential outcomes consistent with estimation results, descriptive analysis, and economic reasoning. Computational resources are provided with two R packages. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Abacus is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Abacus holder: Wiley-Blackwell dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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