When to Use Bar Graphs and PIe Charts

What is it about?

In aseparate article on figures, I discussed line graphs and scattergrams, 2 of the most widely used approaches for presenting data and results in scientific papers. In line graphs and scattergrams, each axis is of a continuous variable. For example, the x axis may show a continuous range of phenytoin doses, and the y axis may show the corresponding range of the resulting serum phenytoin concentrations. Or, the x axis may be a range of months after chemotherapy, and the y axis may be the percentage of surviving patients. Assay comparisons, chromatograms, ROC curves, and PCR amplification curves are all examples of line graphs or scattergrams. There are, however, situations in which the variables are discontinuous (also called “discrete” or “nominal” variables), meaning that they are categorically different (e.g., eye color); combined within an interval (e.g., ages 21–30 years, 31–40 years, 41–50 years); or numerically scaled (ordinal variables) (e.g., tumor stages 1, 2, 3, and 4). When a visual representation of the results for discontinuous variables is desired, 2 commonly used approaches are bar graphs and pie charts. In this article, I discuss the pros and cons of these 2 types of figures.

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Why is it important?

Sometimes bar graphs and pie charts are the preferred way to illustrate your data and results. However, most authors make these types of figures/images too complex and defeat the purpose of using a bar graph or pie chart in the first place. Following certain visual formats can add "psychological appeal" to your data and results.