December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
How bar graphs deceive: readout-based measurement reveals three fallacies
Author Affiliations & Notes
  • Jeremy Wilmer
    Wellesley College
  • Sarah Kerns
    Keio University
  • Footnotes
    Acknowledgements  This research was funded in part by NSF award #1624891 to JBW, a Brachman Hoffman grant to JBW, and a sub-award from NSF grant #1837731 to JBW.
Journal of Vision December 2022, Vol.22, 3968. doi:
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      Jeremy Wilmer, Sarah Kerns; How bar graphs deceive: readout-based measurement reveals three fallacies. Journal of Vision 2022;22(14):3968.

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      © ARVO (1962-2015); The Authors (2016-present)

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A substantial portion of all data insights are conveyed via mean values; and mean values are commonly depicted via bar graphs. Bar graphs of mean (BGoMs) are frequently presumed to be accessible to non-experts. Yet evidence for or against this presumption remains sparse. Here, we use a readout-based measurement approach developed by our lab (Wilmer & Kerns, 2021) to document three fundamental fallacies in the interpretation of BGoMs. A readout is a relatively concrete, detailed, uninterpreted record of thought, typically produced via pencil-and-paper drawing. In the present study, each of 133 demographically diverse participants sketched stimulus BGoMs along with their best guess of the individual datapoints that were averaged to produce the shown mean values. As a test of reproducibility, each participant completed drawings for four stimulus graphs that were selected to represent diverse content areas (developmental, clinical, social, cognitive), data types (questionnaire, performance), and BGoM forms (unidirectional, bidirectional) within the broad field of psychology. The three observed fallacies were: (1) a Bar-Tip Limit Error (data plotted inside the bar, rather than spread across the bar-tip, as if the bar represented counts instead of means), (2) a Dichotomization Fallacy (complete non-overlap, or dichotomy, between distributions that should overlap), and (3) a Uniformity Fallacy (data distributed uniformly, rather than in normal (Gaussian) form). These fallacies were largely independent from each other. While they varied somewhat in prevalence between stimulus graphs, each was common and consistently displayed by individuals across graph stimuli. Together, they impacted 52% to 83% of readouts, depending on the stimulus graph. The existence of multiple common severe fallacies in the interpretation of BGoMs raises serious questions about the presumed accessibility of BGoMs. The efficiency and clarity with which these fallacies are revealed by our readout-based approach suggests that readout-based measurement holds promise for the study of graph cognition.


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