Open AccessReveal, Don’t Conceal
Author(s) -
Tracey L. Weissgerber,
Stacey J. Winham,
Ethan P. Heinzen,
Jelena Milin-Lazović,
Oscar A. Garcia Valencia,
Zoran Bukumirić,
Marko Savić,
Vesna D. Garovic,
Nataša Milić
Publication year - 2019
Publication title -
circulation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.795
H-Index - 607
eISSN - 1524-4539
pISSN - 0009-7322
DOI - 10.1161/circulationaha.118.037777
Subject(s) - bar chart , data science , pie chart , graphics , visualization , computer science , data visualization , box plot , sample size determination , sample (material) , data mining , publication , information retrieval , statistics , computer graphics (images) , chemistry , mathematics , chromatography , advertising , business
Reports highlighting the problems with the standard practice of using bar graphs to show continuous data have prompted many journals to adopt new visualization policies. These policies encourage authors to avoid bar graphs and use graphics that show the data distribution; however, they provide little guidance on how to effectively display data. We conducted a systematic review of studies published in top peripheral vascular disease journals to determine what types of figures are used, and to assess the prevalence of suboptimal data visualization practices. Among papers with data figures, 47.7% of papers used bar graphs to present continuous data. This primer provides a detailed overview of strategies for addressing this issue by (1) outlining strategies for selecting the correct type of figure depending on the study design, sample size, and the type of variable; (2) examining techniques for making effective dot plots, box plots, and violin plots; and (3) illustrating how to avoid sending mixed messages by aligning the figure structure with the study design and statistical analysis. We also present solutions to other common problems identified in the systematic review. Resources include a list of free tools and templates that authors can use to create more informative figures and an online simulator that illustrates why summary statistics are meaningful only when there are enough data to summarize. Last, we consider steps that investigators can take to improve figures in the scientific literature.