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Scaling visualized data using common multiples

How we’re presented with data skews how we interpret that data. Anyone who has read a lick of Tufte (or simply tried to make sense of a chart in USA Today) knows this. Many such commonly-encountered misrepresentations tend to relate to scaling. Volatility in a trend line minimized by a reduction in scale, perspective distortion in a 3D pie chart, a pictograph being scaled in two dimensions such that its visual weight becomes disproportionate – technically, the graphic may accurately line up with the values to be conveyed, but visually the message is lost.

In taking mandatory domestic violence training at work recently, I was thrown completely off by a statistic and accompanying graphic. The graphic (albeit using masculine and feminine silhouette images) resembled:

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…with the corresponding statistic that one in every four women and one in every seven men has experienced domestic violence. While the numbers made sense to me, it took me a while to grasp them, because the graphic was simply showing more men than women. It’s something akin to a scale problem, our brains are going to see the obvious numbers – four and seven – instead of readily converting the graphics into their respective percentages. How to fix this, then?

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If we simply repeat our graphics such that the total number in either set is a common multiple, now it’s much simpler to process the information that we’re supposed to process. We might not immediately recognize that seven in twenty-four is the same as one in four, nor that four in twenty-four is the same as one in seven, but we do know that seven is greater than four (which caused the problem in the first place earlier), and now we’re not dealing with mentally constructing percentages from a simple visual.