dc.date.accessioned 2019-05-21T12:18:35Z dc.date.available 2019-05-21T12:18:35Z dc.date.issued 2019-02-20 dc.identifier doi:10.17170/kobra-20190521501 dc.identifier.uri http://hdl.handle.net/123456789/11238 dc.description.sponsorship Gefördert durch den Publikationsfonds der Universität Kassel dc.language.iso eng dc.subject epistemic uncertainty eng dc.subject Bayesian situations eng dc.subject judgment and decision making eng dc.subject visualization of statistical information eng dc.subject nested-sets structure eng dc.subject.ddc 510 dc.title How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations eng dc.type Aufsatz dcterms.abstract Bayes’ formula is a fundamental statistical method for inference judgments in uncertain situations used by both laymen and professionals. However, since people often fail in situations where Bayes’ formula can be applied, how to improve their performance in Bayesian situations is a crucial question. We based our research on a widely accepted beneficial strategy in Bayesian situations, representing the statistical information in the form of natural frequencies. In addition to this numerical format, we used five visualizations: a 2 × 2-table, a unit square, an icon array, a tree diagram, and a double-tree diagram. In an experiment with 688 undergraduate students, we empirically investigated the effectiveness of three graphical properties of visualizations: area-proportionality, use of discrete and countable statistical entities, and graphical transparency of the nested-sets structure. We found no additional beneficial effect of area proportionality. In contrast, the representation of discrete objects seems to be beneficial. Furthermore, our results show a strong facilitating effect of making the nested-sets structure of a Bayesian situation graphically transparent. Our results contribute to answering the questions of how and why a visualization could facilitate judgment and decision making in situations of uncertainty. eng dcterms.accessRights open access dcterms.creator Böcherer-Linder, Katharina dcterms.creator Eichler, Andreas dc.relation.doi doi:10.3389/fpsyg.2019.00267 dc.type.version publishedVersion dcterms.source.identifier ISSN: 1664-1078 dcterms.source.journal Frontiers in psychology dcterms.source.pageinfo 267 dcterms.source.volume 10
﻿