BEWARE OF INFERENTIAL ERRORS AND LOW POWER WITH BAYESIAN ANALYSES: POWER ANALYSIS IS NEEDED FOR CONFIRMATORY RESEARCH.
Errors in inference can occur with any hypothesis testing method, including Bayesian analysis. The evaluation of expected rates of inferential errors is important when planning confirmatory research, but inferential errors have rarely been addressed in writings on Bayesian hypothesis testing. The pr...
| Publicado en: | Journal of Parapsychology Vol. 79; no. 1; pp. 53 - 65 |
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| Formato: | Artículo |
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Rhine Research Center
Spring2015
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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=ssf&AN=109192143&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 109192143 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223387 PAY jtl: Journal of Parapsychology issn: 00223387 maglogo: N pubinfo: dt: Spring2015 vid: 79 iid: 1 pid: 533 pub: Rhine Research Center artinfo: ui: 109192143 ppf: 53 ppct: 12 formats: fmt: @attributes: type: P size: 2MB tig: atl: BEWARE OF INFERENTIAL ERRORS AND LOW POWER WITH BAYESIAN ANALYSES: POWER ANALYSIS IS NEEDED FOR CONFIRMATORY RESEARCH. aug: au: Kennedy, J. E. su: Quantitative research Inferential statistics Errors Bayesian analysis Probability theory sug: subj: Quantitative research Inferential statistics Errors Bayesian analysis Probability theory keyword: confirmatory research hypothesis test inferential errors statistical power confirmatory research hypothesis test inferential errors statistical power ab: Errors in inference can occur with any hypothesis testing method, including Bayesian analysis. The evaluation of expected rates of inferential errors is important when planning confirmatory research, but inferential errors have rarely been addressed in writings on Bayesian hypothesis testing. The present investigation applied classical and Bayesian hypothesis testing methods to binomial data with certain effects and to data simulating the null hypothesis. The Bayesian analyses generally had substantially lower power (probability of correctly detecting an effect), particularly for small effect sizes. For data with a small effect size and power of .80 for a classical analysis, the probability that the Bayes factor with a uniform prior correctly reached 3 or higher supporting the alternative model was only .173. The probability that the Bayes factor was 3 or higher incorrectly supporting the null model was .619. These findings verify that quantitative evaluation of expected inferential error rates is essential when designing confirmatory studies that use Bayesian analyses. The argument that biases in favor of the null model are appropriate for small effect sizes because of potential methodological problems is based on exploratory research and is not appropriate for well-designed confirmatory research that focuses on a pre-established effect size. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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