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.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Posted September 24, 2024.
