recommended by: Koichi Sugimoto
Koichi says be sure to check out the resources provided in the supplemental information (See “Conclusions” after the Abstract below).
Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm
Tracey L. Weissgerber et al (2015) PLoS Biology; DOI: 10.1371/journal.pbio.1002128
Abstract
Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.
Conclusions
Our systematic review identified several critical problems with the presentation of continuous data in small sample size studies. A coordinated effort among investigators, medical journals, and statistics instructors is recommended to address these problems. We created free Excel templates (S2 Text and S3 Text, https://www.ctspedia.org/do/view/CTSpedia/TemplateTesting) that will allow researchers to quickly make univariate scatterplots for independent data (with or without overlapping points) and nonindependent data. We hope that improved data presentation practices will enhance authors’, reviewers’, and readers’ understanding of published data by ensuring that publications include the information needed to critically evaluate continuous data in small sample size studies.
