December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Bar graphs of mean values produce inflated and variable estimates of effect size
Author Affiliations & Notes
  • Ja Youn Lee
    Wellesley College
  • Sarah Kerns
    University of Waterloo
  • Jeremy Wilmer
    DCS Corp., Alexandria, VA USA
  • 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, 4432. doi:
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      Ja Youn Lee, Sarah Kerns, Jeremy Wilmer; Bar graphs of mean values produce inflated and variable estimates of effect size. Journal of Vision 2022;22(14):4432.

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

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Bar graphs of mean values (BGoMs) are frequently criticized for abstracting beyond, and thus hiding, the individual values that are averaged to produce their plotted mean values. Yet does this abstraction produce miscommunication? BGoMs are often presumed, due to their visual simplicity, to communicate well, especially to non-expert viewers. Here, we tested that presumption. In a study of 29 non-expert viewers, we first found that viewers overestimated the effect sizes conveyed by two real BGoMs taken directly from popular Introductory Psychology textbooks. We then asked whether manipulation of the y-axis range would reduce this overestimation and found that it did, but only partly, and only in some viewers. We measured estimated effect sizes in Cohen’s d (SD) units via a drawing-based method developed by our lab that requires no prior statistical knowledge (Kerns & Wilmer, 2021). Participants simply sketch a version of a viewed BGoM, adding hypothesized data points that, when averaged, would produce the plotted mean values. For two BGoMs whose real effect sizes were 1.0 and 0.7, the median drawn effect sizes were 4.4 and 9.5. Expansion of the y-axis range reduced, but did not eliminate the overestimation (median effect sizes were 3.2 and 2.1, respectively, for a 2x expansion, and 3.7 and 2.5, respectively, for a 4x expansion). Moreover, the variation between the largest and smallest drawn effect size in every condition (2 BGoMs x 3 y-axis ranges) represented at least a fivefold difference; therefore, though overestimation was reduced on average, different viewers still came away with markedly different, often highly inaccurate, conceptions of the data. We conclude that BGoMs are capable of producing distorted, highly varied interpretations of data in non-expert viewers, and that abstraction in BGoMs is not just a theoretical concern, but an evidence-based concern.


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