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September 10, 2025Open Access

Creating and styling boxplots

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Overview

This resource demonstrates how to create boxplots to effectively display distributions in data, highlighting outliers.

Key Points

  • Boxplots visually summarize the distribution of a continuous variable, making it easier to understand data patterns.
  • They effectively identify outliers, which are individual data points that significantly deviate from the rest of the dataset.
  • The structure of boxplots includes a box representing the interquartile range and lines ('whiskers') that extend to non-outlier data points.
  • Utilizing boxplots can enhance data presentations, making insights clearer and supporting data-driven decisions.

Cite This Study

A 2023 study studied this question.

synapsesocial.com/papers/68c1e24854b1d3bfb60ff164https://doi.org/10.59350/1shx4-jvf47
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Box Plots: An Introduction to the Visual EDA Wonder2026
  2. 2The Bag-and-Whisker Plot: A New Bagplot for Bivariate Data2026
  3. 3Speaking Stata: Quantile-box plots and beyond: Variations on a theme by Emanuel Parzen2026
  4. 4Graphical Representation of Analytical Data2009
  5. 5<tt> <b>ChauBoxplot</b> </tt> and <b> <tt>AdaptiveBoxplot</tt> </b> : two R packages for boxplot-based outlier detection2026 · 2 citations