2020
DOI: 10.1007/s00210-020-01926-x
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Choice of y-axis can mislead readers

Abstract: Using two examples from the non-scientific literature, we show how choice of unit of measure and scaling of y-axis can caused a biased perception of data, a phenomenon we propose to call perception bias. We recommend to pre-specify unit of measure or how it will be determined, whether outcome variables will be shown as absolute or relative/normalized changes, and to typically start y-axis at 0 for ratio variables.

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Cited by 3 publications
(3 citation statements)
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“…Data are expressed as mean ± SD and shown as bar graphs overlaid with scatter plots for increased transparency [49]. To avoid misleading y-axis scaling, all y-axes start at 0 [50]. All statistical analyses were performed with Prism (version 9, GraphPad, La Jolla, CA, USA).…”
Section: Discussionmentioning
confidence: 99%
“…Data are expressed as mean ± SD and shown as bar graphs overlaid with scatter plots for increased transparency [49]. To avoid misleading y-axis scaling, all y-axes start at 0 [50]. All statistical analyses were performed with Prism (version 9, GraphPad, La Jolla, CA, USA).…”
Section: Discussionmentioning
confidence: 99%
“…We arbitrarily assumed that a sex difference of >10% could be considered as biologically relevant. Based on previous recommendation ( Erdogan et al, 2020 ), we used the same y-axis spread for all graphs showing a parameter to avoid bias in reporting.…”
Section: Methodsmentioning
confidence: 99%
“…An alarmingly high fraction of published research in experimental biomedicine has been found not to be reproducible or replicable (Freedman et al 2015). Other than biases at the level of study planning and conduct, data analysis, and reporting (Szafir 2018;Erdogan et al 2020;Vollert et al 2020), a poor understanding and inappropriate use of statistical analysis is a prevalent cause of poor reproducibility of findings in the experimental life sciences (Colquhoun 2019;Wasserstein et al 2019;Michel et al 2020). As highlighted very recently, inappropriate use of statistical approaches could even lead to the invalidation of issued patents (Curfman et al 2020).…”
Section: Introductionmentioning
confidence: 99%