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Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data

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  • Myeong-Hun Jeong
  • Matt Duckham
  • Susanne Bleisch

Abstract

This research investigates the performance of graphical dot arrays designed to make discrimination of relative numerosity as effortless as possible at the same time as making absolute (quantitative) numerosity estimation as effortful as possible. Comparing regular, random, and hybrid (randomized regular) configurations of dots, the results indicate that both random and hybrid configurations reduce absolute numerosity estimation precision, when compared with regular dots arrays. However, discrimination of relative numerosity is significantly more accurate for hybrid dot arrays than for random dot arrays. Similarly, human subjects report significantly lower levels of subjective confidence in judgments when using hybrid dot configurations as compared with regular configurations; and significantly higher levels of subjective confidence as compared with random configurations. These results indicate that data graphics based on the hybrid, randomized-regular configurations of dots are well-suited to applications that require decisions to be based on numerical data in which the absolute quantities are less certain than the relative values. Examples of such applications include decision-making based on the outputs of empirically-based mathematical models, such as health-related policy decisions using data from predictive epidemiological models.

Suggested Citation

  • Myeong-Hun Jeong & Matt Duckham & Susanne Bleisch, 2015. "Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    • Handle: RePEc:plo:pone00:0141271
    DOI: 10.1371/journal.pone.0141271
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    References listed on IDEAS

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    1. Rocio Garcia-Retamero & Mirta Galesic & Gerd Gigerenzer, 2010. "Do Icon Arrays Help Reduce Denominator Neglect?," Medical Decision Making, , vol. 30(6), pages 672-684, November.
    2. Amir Homayoun Javadi & Clarisse Aichelburg, 2013. "Training Enhances the Interference of Numerosity on Duration Judgement," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    3. Titia Gebuis & Bert Reynvoet, 2012. "The Role of Visual Information in Numerosity Estimation," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-5, May.
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