PT - JOURNAL ARTICLE AU - Lin, Teng-Jui AU - Landry, Markita P. TI - Quantifying Data Distortion in Bar Graphs in Biological Research AID - 10.1101/2024.09.20.609464 DP - 2024 Jan 01 TA - bioRxiv PG - 2024.09.20.609464 4099 - http://biorxiv.org/content/early/2024/09/24/2024.09.20.609464.short 4100 - http://biorxiv.org/content/early/2024/09/24/2024.09.20.609464.full 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.