Charles Wheelan

And it is not merely a hypothetical case. Evolutionary biologist Stephen Jay Gould was diagnosed with a form of cancer that had a median survival time of eight months; he died of a different and unrelated kind of cancer twenty years later.3 Gould subsequently wrote a famous article called “The Median Isn’t the Message,” in which he argued that his scientific knowledge of statistics saved him from the erroneous conclusion that he would necessarily be dead in eight months. The definition of the median tells us that half the patients will live at least eight months—and possibly much, much longer than that. The mortality distribution is “right-skewed,” which is more than a technicality if you happen to have the disease.4

“if you had studied, the material would look a lot more familiar.” This was a compelling point

In hindsight, I now recognize that it wasn’t the math that bothered me in calculus class; it was that no one ever saw fit to explain the point of it

computer can do sophisticated statistical procedures with a few keystrokes. The problem is that if the data are poor, or if the statistical techniques are used improperly, the conclusions can be wildly misleading and even potentially dangerous

Now, having just made the case that the core tools of statistics are less intuitive and accessible than they ought to be, I’m going to make a seemingly contradictory point: Statistics can be overly accessible in the sense that anyone with data and a computer can do sophisticated statistical procedures with a few keystrokes. The problem is that if the data are poor, or if the statistical techniques are used improperly, the conclusions can be wildly misleading and even potentially dangerous. Consider the following hypothetical Internet news flash: People Who Take Short Breaks at Work Are Far More Likely to Die of Cancer. Imagine that headline popping up while you are surfing the Web. According to a seemingly impressive study of 36,000 office workers (a huge data set!), those workers who reported leaving their offices to take regular ten-minute breaks during the workday were 41 percent more likely to develop cancer over the next five years than workers who don’t leave their offices during the workday. Clearly we need to act on this kind of finding—perhaps some kind of national awareness campaign to prevent short breaks on the job.

Or maybe we just need to think more clearly about what many workers are doing during that ten-minute break. My professional experience suggests that many of those workers who report leaving their offices for short breaks are huddled outside the entrance of the building smoking cigarettes (creating a haze of smoke through which the rest of us have to walk in order to get in or out). I would further infer that it’s probably the cigarettes, and not the short breaks from work, that are causing the cancer. I’ve made up this example just so that it would be particularly absurd, but I can assure you that many real-life statistical abominations are nearly this absurd once they are deconstructed.

Statistics is like a high-caliber weapon: helpful when used correctly and potentially disastrous in the wrong hands.

I hope to persuade you of the observation first made by Swedish mathematician and writer Andrejs Dunkels: It’s easy to lie with statistics, but it’s hard to tell the truth without them

Is the quarterback rating perfect? No. Statistics rarely offers a single “right” way of doing anything. Does it provide meaningful information in an easily accessible way? Absolutely

The Gini index measures how evenly wealth (or income) is shared within a country on a scale from zero to one. The statistic can be calculated for wealth or for annual income, and it can be calculated at the individual level or at the household level. (All of these statistics will be highly correlated but not identical.)

As you can probably surmise, the closer a country is to one, the more unequal its distribution of wealth. The United States has a Gini index of .45, according to the Central Intelligence Agency (a great collector of statistics, by the way).1 So what?

Once that number is put into context, it can tell us a lot. For example, Sweden has a Gini index of .23. Canada’s is .32. China’s is .42. Brazil’s is .54. South Africa’s is .65.*

What is the point? The point is that statistics helps us process data, which is really just a fancy name for information