期刊
FRONTIERS IN PSYCHOLOGY
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fpsyg.2019.00267
关键词
epistemic uncertainty; Bayesian situations; judgment and decision making; visualization of statistical information; nested-sets structure
资金
- Ministry of Science, Research and the Arts of the State of Baden-Wurttemberg, Germany
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 x 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.
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