This research paper quantifies data distortion in bar graphs frequently used in biological research publications. The authors analyzed 3387 articles, finding that 29% contained mistakes, primarily "zeroing" and "log" errors, which significantly misrepresent data. They developed a mathematical framework to measure this distortion and propose recommendations to improve data visualization literacy and publication standards. The study highlights the need for better data science training to mitigate these issues and prevent misinterpretations of scientific findings.