[Florence Nightingale]“That makes her perhaps the first person to grasp that busy, influential people would pay far more attention to a vivid diagram than to a table of numbers.
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As Nightingale quipped when sending one of her analytical books to the Queen, “She may look at it because it has pictures.”
It’s a cynical, almost contemptuous, thing to write. But it is true. A chart has a special power. Our visual sense is potent, perhaps too potent. The word “see” is often used as a direct synonym for “understand”—“I see what you mean.” Yet sometimes we see but we don’t understand; worse, we see, then “understand” something that isn’t true at all. Done well, a picture of data is worth the proverbial thousand words. It is more than persuasive; it shows us things we could not have seen before, revealing patterns amid chaos. However, much depends on the intent of the chart’s creator, and the wisdom of the reader.
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Much of the data visualization that bombards us today is decoration at best, and distraction or even disinformation at worst. The decorative function is surprisingly common, perhaps because the data visualization teams of many media organizations are part of the art departments. They are led by people whose skills and experience are not in statistics but in illustration or graphic design. The emphasis is on the visualization, not on the data. It is, above all, a picture.
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So information is beautiful—but misinformation can be beautiful, too. And producing beautiful misinformation is becoming easier than ever.
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A good chart isn’t an illustration but a visual argument ... If a good chart is a visual argument, a bad chart may be a confusing mess—or it may also be a visual argument, but a deceptive and seductive one. Either way, by organizing and presenting the data, we are inviting people to draw certain conclusions. And just as a verbal argument can be logical or emotional, sharp or woolly, clear or baffling, honest or misleading, so too can the argument made by a chart.
I should note here that not all good charts are visual arguments. Some data visualization is not intended to be persuasive, but exploratory. If you’re handling a complex dataset, you’ll learn a lot by turning it into a few different graphs to see what they show. Trends and patterns will often leap out immediately if plotted in the right way."
Por exemplo, no livro que ando a ler "Unsettled - What Climate Science Tells Us. What it Doesn't, and Why it Matters" de Steven E. Koonin", estou farto de encontrar exemplos como este:
Trechos retirados de "The Data Detective" de Tim Harford.
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