How can you handle outliers in data visualization?

1 Identify outliers

The first step is to identify outliers in your data. You can use descriptive statistics, such as mean, median, standard deviation, and quartiles, to get a sense of the central tendency and dispersion of your data. You can also use graphical methods, such as box plots, histograms, scatter plots, and density plots, to visually inspect your data for outliers. You can also apply formal tests, such as z-scores, t-tests, or Grubbs' test, to detect outliers based on statistical criteria.

2 Analyze outliers

The next step is to analyze outliers and understand their causes and implications. You should ask yourself questions such as whether the outliers are due to measurement or data entry errors, natural variability or inherent characteristics of the data, or anomalies or rare events. Additionally, consider how the outliers affect the distribution and summary statistics of your data, such as skewing or inflating the mean, variance, or standard deviation, or changing the shape or symmetry of your data.

3 Choose appropriate visualizations

Choosing appropriate visualizations for handling outliers without compromising the overall message and clarity of your graphs is the third step. When making this choice, you should consider the type and scale of your data, the purpose and audience of your visualization, and the trade-offs and alternatives of your visualization. For instance, if you want to emphasize the outliers or show the full range of your data, scatter plots, box plots, or violin plots may be used; if you want to focus on the main trends or patterns of your data, histograms, bar charts, or line charts may be more suitable. Additionally, if you use visualizations that include outliers, you may need to adjust the axis limits, labels, or annotations to avoid distortion or clutter; if you use visualizations that exclude outliers, you may need to report them separately or use other methods such as trimming, winsorizing, or transforming to reduce their impact.

4 Evaluate and communicate your visualization

The final step is to evaluate and communicate your visualization and the outliers in your data. You should check the accuracy and validity of your visualization to ensure it reflects the true nature and distribution of your data, accounting for uncertainty and variability. Additionally, assess the clarity and simplicity of your visualization, using appropriate colors, shapes, sizes, and symbols to convey the main message and insights of your data. Lastly, consider the context and explanation of your visualization. Make sure it provides relevant background information for your data, explains the outliers and their causes and effects, as well as uses appropriate titles, captions, legends, and sources. Outliers are an unavoidable part of data analysis and visualization. By following these steps, you can effectively manage them in your visualizations.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?