Bar charts are ubiquitous in the life-science literature, yet a study suggests that they’re often used in ways that can misrepresent research findings. The preprint1, which was first posted on bioRxiv in September and has yet to be peer reviewed, found that in a collection of nearly 3,400 papers from 2023 that included at least one bar chart, almost one-third distorted the data in some way, highlighting a need for increased data literacy among scientists and for a system of checks throughout the writing and publishing processes.
“Data are getting more complex all the time, and data literacy doesn’t always keep pace with that. Even still, I do think it’s surprising how common some of these mistakes are,” says Rebecca Goldin, a mathematician at George Mason University in Fairfax, Virginia. “It’s good to see more attention being paid to how we visualize our work.”
‘Spell-checker for statistics’ reduces errors in the psychology literature
To quantify the amount of data distortion she often saw in published bar charts, Markita Landry, a nanobiotechnology researcher at the University of California, Berkeley, and her colleague Teng-Jui Lin analysed 3,387 life-science papers published in 15 journals last year, including several Nature and Science journals, as well as Cell, Bioengineering & Translational Medicine and ACS Nano.
The pair found that 88% of the papers contained at least one bar chart; of those papers, 29% had a bar chart with some form of data distortion. The most common types of distortion included failing to start the y axis at zero, as well as mistakes that related to logarithmic axes. The former often inflates the difference between two values to make small disparities look larger, and the latter can minimize differences because our brains are prone to perceive scales as linear. Papers with multiple co-authors were most likely to include these distortions, the team found. “More collaborators may make it easier for mistakes to fall through the cracks,” Landry explains.
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