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超越柱状图和折线图:是时候采用新的数据呈现范式了。

Beyond bar and line graphs: time for a new data presentation paradigm.

作者信息

Weissgerber Tracey L, Milic Natasa M, Winham Stacey J, Garovic Vesna D

机构信息

Division of Nephrology & Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America.

Division of Nephrology & Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia.

出版信息

PLoS Biol. 2015 Apr 22;13(4):e1002128. doi: 10.1371/journal.pbio.1002128. eCollection 2015 Apr.

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.

摘要

科学出版物中的图表至关重要,因为它们常常展示支持关键发现的数据。我们对发表在顶级生理学杂志上的研究文章(n = 703)进行的系统综述表明,作为科学家,我们迫切需要改变在小样本量研究中呈现连续数据的做法。论文很少包含能让读者严格评估连续数据的散点图、箱线图和直方图。大多数论文以条形图和折线图呈现连续数据。这存在问题,因为许多不同的数据分布可能导致相同的条形图或折线图。完整数据可能会得出与汇总统计数据不同的结论。我们建议对研究人员进行数据呈现方面的培训,鼓励更完整地呈现数据,并改变期刊编辑政策。研究人员可以使用我们的Excel模板快速为小样本量研究制作单变量散点图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5855/4406565/67a4916cbf45/pbio.1002128.g001.jpg

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