ArticleFrontiers in psychology2019
How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations.
Article in Frontiers in psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Analyzing and supporting mental representations and strategies in solving Bayesian problems.Frontiers in psychology · 2026Article
- Graphical Perception of Icon Arrays versus Bar Charts for Value Comparisons in Health Risk Communication.IEEE transactions on visualization and computer graphics · 2026Article
- The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review.Vision (Basel, Switzerland) · 2023Review
- Article
- Analyzing and supporting mental representations and strategies in solving Bayesian problems.Frontiers in psychology · 2023Article
- Measuring people's covariational reasoning in Bayesian situations.Frontiers in psychology · 2023Article
- Tversky and Kahneman's Cognitive Illusions: Who Can Solve Them, and Why?Frontiers in psychology · 2021Article
- Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies.Frontiers in psychology · 2020Review
- Different Visualizations Cause Different Strategies When Dealing With Bayesian Situations.Frontiers in psychology · 2020Article
- A New Visualization for Probabilistic Situations Containing Two Binary Events: The Frequency Net.Frontiers in psychology · 2020Article
- Scope, Methods, and Overview Findings for the Making Numbers Meaningful Evidence Review of Communicating Probabilities in Health: A Systematic Review.MDM policy & practiceReview
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.