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dc.contributor.authorDavis, Christopher
dc.contributor.authorMcQuestion, Jack
dc.date.accessioned2017-12-11T18:20:39Z
dc.date.available2017-12-11T18:20:39Z
dc.date.issued2017-12-11T18:20:39Z
dc.identifier.urihttp://digital.library.wisc.edu/1793/77561
dc.descriptionColor poster with text and graphs.en
dc.description.abstractConventional frequentist statistics taught in undergraduate courses are obviously better than nothing, yet suffer from systematic failures that allow for easy p-hacking and consistent over-estimation of significance and effect sizes. Use of frequentist statistics is the strongest factor contributing to the replication crisis in the social sciences. Bayesian statistics, by contrast, have numerous advantages and can be tailored to any possible inferential problem. Many researchers and academics have advocated for Bayesianism, but only relatively recently have advances in computer technology allowed certain statistical methods to become practical.en
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen
dc.language.isoen_USen
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectMathematicsen
dc.subjectBayesian statisticsen
dc.subjectStatisticsen
dc.subjectPostersen
dc.titleBayesian Inferential Statistics Implemented in Ren
dc.typePresentation


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    Posters of collaborative student/faculty research presented at CERCA

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