This chart is trying to trick you
⚠ Warning: this chart is lying to you!
🍬 The original chart in this example is trying to suggest a strong correlation between sugar intake and obesity in the US between 1980 and 2000. It does so by carefully choosing the vertical axis ranges and scaling so both lines nicely fall on top of each other.
But with a closer look we can see something else is going on. Sugar intake levels are rising by 30% (from 85g to 110g), while obesity prevalence is rising by 164% (from 14% to 37% of the population). For an accurate comparison, these lines shouldn’t nicely align at all!
I’ve created two redesigns to present some better solutions for this visualization. In redesign 1 we focus on a presentation which is as truthful as possible, comparing the data with the recommended intake level, and enabling an accurate estimation of the prevalence %.
In redesign 2 we focus on showing how much faster the obesity prevalence has grown compared to the sugar intake, which has remained relatively stable.
Depending on the message you want to bring, one presentation might be preferable above the other. But in any case, manipulating your vertical axes to suggest a strong correlation which might not be there, is not very nice!
Still struggling with telling a strong visual message using truthful charts? Find out how we can help you, or reach out to us directly.
Read more:
Behind the maps
In the 30-day Map Challenge, you are challenged to design a new map every day around a certain topic. I participated in November 2020, and wrote this post to share my thought processes, data sources, tools and results!
Data visualization resources: all the links you’ll ever need!
You want to start creating clear and attractive data visuals, but don't know where to start? No worries, here's a complete overview of tools, resources and inspiration you can use as a starting point for your designs.
Storytelling with Data: Dataviz book review
The Storytelling with Data book has been on my wishlist as long as I can remember, because so many people recommend it as one of the must read dataviz books. So let's see what the fuzz is all about - here's my review!
Uncommon chart types: Slopegraphs
Slopegraphs appear in 'serious' newspapers, but they are very easy to create yourself. Use them if you want to compare how values have changed between two different points in time!
Data visualization in a time of pandemic – #6: Viral scrollytelling
In this final chapter, we’ll dive deeper into some of the insightful stories which have been published about the novel coronavirus and the COVID-19 pandemic. Rather than looking at single charts, we’ll highlight some long-form stories about the origin of the virus, how it works, and how it spread.
Five steps towards improving your dashboard
Today I would like to share with you the five steps I usually follow when I analyze and improve dashboards. If you are planning to analyze and improve your own dashboard, or maybe the dashboard someone else created and you want to provide feedback on, you could follow these five steps as well.
We are really into visual communication!
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