RT Journal Article SR Electronic T1 Quantifying Data Distortion in Bar Graphs in Biological Research JF bioRxiv FD Cold Spring Harbor Laboratory SP 2024.09.20.609464 DO 10.1101/2024.09.20.609464 A1 Lin, Teng-Jui A1 Landry, Markita P. YR 2024 UL http://biorxiv.org/content/early/2024/09/24/2024.09.20.609464.abstract AB 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 StatementThe authors have declared no competing interest.