This chart is trying to trick you
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:
Choosing the right font for your data visual
Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.
Three roles of colour in a data visual
Colour is one of the most crucial tools we have to turn a normal chart into a powerful chart with a clear message, a chart which tells a story rather than simply presenting the information.
7 different goals for your chart
A crucial step in building a powerful chart is choosing the right type of chart. A lot of charts don’t work because they simply use the wrong type of chart. To avoid this trap, we must ask ourselves a basic question: what’s the ultimate goal of our data visual? What do we want to show with our data?
Making a data visual noise-free
Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.
Three tips to create powerful charts in Excel
Creating charts in Excel can be a very powerful tool for making sense of complex data sets, and for visualizing them. But the default options are not always the most pretty or effective ones. Here are our top three tips to create better Excel charts.
A powerful chart has a high signal-to-noise ratio
‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message - our signal - should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message - the noise - should be removed.
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