What Makes Box Plots a Misleading Choice for Data Analysis?

...and here's how to prevent being misled by them.

Box plots are quite common in data analysis.

Yet, they can be highly misleading at times.

To begin, a box plot is a graphical representation of just five numbers:

• min

• first quartile

• median

• third quartile

• max

Thus, entirely different distributions with similar five values will have identical box plots.

This is evident from the image below:

Three different datasets have the same box plots.

Thus, solely looking at a bar plot may lead to incorrect or misleading conclusions.

Here, the takeaway is not that box plots should not be used. Instead, it’s similar to what we saw in one of the earlier posts about correlation:

Why Correlation (and Other Summary Statistics) Can Be Misleading

Whenever you generate any summary statistic, you lose essential information.

Thus, it is always important to look at the underlying data distribution.

For instance, whenever I create a box plot, I create a violin (or KDE) plot too. This lets me validate whether summary statistics resonate with the data distribution.

👉 Over to you: What other measures do you take when using summary statistics?

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