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What Makes Histograms a Misleading Choice for Data Visualisation?
...and here's how to prevent being misled by them.
Histograms are quite common in data analysis and visualisation.
Yet, they can be highly misleading at times.
Let’s understand today!
To begin, a histogram represents an aggregation of one-dimensional data points based on a specific bin width:
This means that setting different bin widths on the same dataset can generate entirely different histograms.
This is evident from the image below:
Each histogram conveys a different story, even though the underlying data is the same.
Thus, solely looking at a histogram to understand the data distribution may lead to incorrect or misleading conclusions.
Here, the takeaway is not that histograms should not be used. Instead, it’s similar to what we saw in one of the earlier posts about correlation:
Whenever you generate any summary statistic, you lose essential information.
In our case, every bin of a histogram also represents a summary statistic — an aggregated count.
And whenever you generate any summary statistic, you lose essential information.
Thus, it is always important to look at the underlying data distribution.
For instance, to understand the data distribution, I prefer a violin (or KDE) plot. This gives me better clarity of data distribution over a histogram.
Visualizing density provides more information and clarity about the data distribution than a histogram.
👉 Over to you: What other measures do you take when using summary statistics?
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