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Hanging Dot Plots and identifying outliers in samples from asymmetrical populations with Hubert and Vandervieren's robust outlier rule

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Abstract Visualizing individual data, empirical distributions, and summary statistics for small to moderate sized samples (≤ 500) is critical in early phases of drug research. We developed a customized SAS® macro, %HangingDotPlot, which displays dot plots with parametric or distribution‐free descriptive statistics and confidence intervals simultaneously on the same page for multiple groups of data. Potential statistical outliers are identified with a fence rule adapted from an adjusted box plot for skewed distributions, which incorporates the medcouple, a robust measure of skewness. WIREs Comput Stat 2013, 5:62–67. doi: 10.1002/wics.1241 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

Example of hanging dot plot.

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Boxplot without skewness adjustment.

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Example 4: a more visually enhanced presentation. Visual analog scale pain score by solution and iontophoresis.

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Example 3: adding descriptive and inferential summary statistics. Visual analog scale pain score by solution and iontophoresis.

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Example 2: displaying the data using the bins on the y‐axis. Visual analog scale pain score by solution and iontophoresis.

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Example 1: a first look at the data (displaying the data AS IS). Visual analog scale pain score by solution and iontophoresis.

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Data for subject 1 at the 80 s time point.

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Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

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