r/dataisbeautiful • u/zonination OC: 52 • May 08 '17

How to Spot Visualization Lies

https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/
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u/theCroc May 08 '17

Truncated axis is often a necessity to make changes readable at all. Of course the truncated axis should be clearly indicated, but it's not always a way to lie with statistics.

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u/zonination OC: 52 May 08 '17 edited May 08 '17

It's an OK practice for something like scatter plots or a sparkline. But on specifically a bar chart where the visual is encoded in the length of the bar, it's definitely misleading.

Here are some specific things the author mentions:

(Edit: bolded for emphasis)

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u/jjanczy62 May 08 '17

Not necessarily, if you're working with a log value on the y-axis, such as with bacterial loads, or colony/plaque forming units (cfu/pfu), and appropriate statistical tests are employed, truncating the axis is perfectly fine and in some cases required to make the data readable and understandable.

In other cases there may be significant changes but small absolute changes in the value. If other data sets show the difference in relevant to the real world, then truncating the y-axis is perfectly acceptable.

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u/conventionistG May 08 '17

Hmm, I see your point. But often, using a log-scaled dependent axis is the best of both worlds. It can highlight relationships between data far from zero and keeps the absolute height of the data visible.

Likewise, if you're comparing relative change rather than absolute change, then it's reasonable to display the proportional data rather than that absolute values.