Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing

Research output: Contribution to journalArticle

Details

Original languageEnglish
Pages (from-to) 2536 - 2545
JournalIEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume18
Issue number12
StatePublished - 2012
MoE publication typeA1 Journal article-refereed

Researchers

Research units

  • INRIA Institut National de Recherche en Informatique et en Automatique
  • University of Kent

Abstract

People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populations with a specific background have been involved. In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. Visualizations included area-proportional Euler diagrams, glyph representations, and hybrid diagrams combining both. Our study failed to replicate previous findings in that subjects' accuracy was remarkably lower and visualizations exhibited no measurable benefit. A second experiment confirmed that simply adding a visualization to a textual Bayesian problem is of little help, even when the text refers to the visualization, but suggests that visualizations are more effective when the text is given without numerical values. We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts.

ID: 10449124