Quantifying Data Distortion in Bar Graphs in Biological Research
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Abstract
Over 88% of biological research articles use bar graphs, of which 29% have undocumented data distortion mistakes that over- or under-state findings. We developed a framework to quantify data distortion and analyzed bar graphs published across 3387 articles in 15 journals, finding consistent data distortions across journals and common biological data types. To reduce bar graph-induced data distortion, we propose recommendations to improve data visualization literacy and guidelines for effective data visualization.
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We suggestinadequate data science education as a key factor behind these issues
In the absence of any evidence favoring one factor or another, I think it would be fair to include other possible factors here (especially as, imo, a lack of data science education is not the most plausible one)
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minimize data misinterpretation
This is a bit of a nitpick, but I'm not sure it's fair to equate "visualization errors" with "data misinterpretation", either on the part of authors or readers, because visualization errors can be made for other reasons by authors and won't necessarily lead to data misinterpretation by readers (even if they do make it more likely)
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The prevalence of misused bar graphs is potentially caused by a systematic lack of data science training inscience, technology, engineering, and mathematics (STEM) education, research training, and the academic
fwiw, another possible explanation that strikes me as more plausible is that there are very strong incentives for authors to publish in prestigious journals, which in turn incentives them to present their results in the most compelling possible light.
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consistent with our hypothesis thathaving more authors increases the probability of having coauthors who contribute visualization mistakes
Perhaps a simpler explanation is that papers with more authors tend to be longer and to have more figures, increasing the chances that at least one figure contains a mistake.
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