Illustration: Ellen Lupton.
Via Wired.
Clive Thompson on Why We Should Learn the Language of Data:
How can global warming be real when there’s so much snow?”
Hearing that question — repeatedly — this past February drove Joseph Romm nuts. A massive snowstorm had buried Washington, DC, and all across the capital, politicians and pundits who dispute the existence of climate change were cackling. The family of Oklahoma senator Jim Inhofe built an igloo near the Capitol and put up a sign reading “Al Gore’s New Home“. The planet can’t be warming, they said; look at all this white stuff!
Romm — a physicist and climate expert with the Center for American Progress — spent a week explaining to reporters why this line of reasoning is so wrong. Climate change, he said, is all about trend lines. You don’t observe it by looking out the window but by analyzing decades’ worth of data. Of course, snowstorm spin is possible only if the public (and journalists) are statistically illiterate. “A lot of this is counterintuitive,” Romm admits.
Statistics is hard. But that’s not just an issue of individual understanding; it’s also becoming one of the nation’s biggest political problems. We live in a world where the thorniest policy issues increasingly boil down to arguments over what the data mean. If you don’t understand statistics, you don’t know what’s going on — and you can’t tell when you’re being lied to. Statistics should now be a core part of general education. You shouldn’t finish high school without understanding it reasonably well — as well, say, as you can compose an essay.
Consider the economy: Is it improving or not? That’s a statistical question. You can’t actually measure the entire economy, so analysts sample chunks of it — they take a slice here and a slice there and try to piece together a representative story. One metric that’s frequently touted is same-store sales growth, a comparison of how much each store in a big retail chain is selling compared with a year ago. It’s been trending upward, which has financial pundits excited.
Problem is, to calculate that stat, economists remove stores that have closed from their sample. As New York University statistician Kaiser Fung points out, that makes the chains look healthier than they might really be. Does this methodological issue matter? Absolutely: When politicians see economic numbers pointing upward, they’re less inclined to fund stimulus programs.
Or take the raging debate over childhood vaccination, where well-intentioned parents have drawn disastrous conclusions from anecdotal information. Activists propagate horror stories of children who seemed fine one day, got vaccinated, and then developed autism. Of course, as anyone with any exposure to statistics knows, correlation is not causation. And individual stories don’t prove anything; when you examine data on the millions of vaccinated kids, even the correlation vanishes.
There are oodles of other examples of how our inability to grasp statistics — and the mother of it all, probability — makes us believe stupid things. Gamblers think their number is more likely to come up this time because it didn’t come up last time. Political polls are touted by the media even when their samples are laughably skewed. (This issue breaks left and right, by the way. Intellectually serious skeptics of anthropogenic climate change argue that the statistical case is weak — that Al Gore and his fellow travelers employ dubious techniques to sample and crunch global temperatures.)
Granted, thinking statistically is tricky. We like to construct simple cause-and-effect stories to explain the world as we experience it. “You need to train in this way of thinking. It’s not easy,” says John Allen Paulos, a Temple University mathematician.
That’s precisely the point. We often say, rightly, that literacy is crucial to public life: If you can’t write, you can’t think. The same is now true in math. Statistics is the new grammar.
