When the assault was over, the assailant “just left.” Andrew remembers resting his head against the bathroom stall and listening to the buzz of the fluorescent lights as he tried to reconcile what had just happened to him.

A knock on the door surprised him. The bathroom required a dorm key, so anyone who lived in the building should have been able to get inside. Andrew opened the door. It was the same student he had seen in the hall.

Around 5 a.m., his classmates returned to their rooms while Andrew headed to the communal bathrooms to brush his teeth. Halfway down the hall, a male student he didn’t recognize passed him. Not thinking much of it, Andrew entered the bathroom and began to wash his hands.

. Moreover, they realize that every statistic is the product of choices -- the choice between defining a category broadly or narrowly, the choice of one measurement over another, the choice of a sample. People choose definitions, measurements, and samples for all sorts of reasons: Perhaps they want to emphasize some aspect of a problem; perhaps it is easier or cheaper to gather data in a particular way -- many considerations can come into play. Every statistic is a compromise among choices. This means that every definition

In order to interpret statistics, we need more than a checklist of common errors. We need a general approach, an orientation, a mind-set that we can use to think about new statistics that we encounter. We ought to approach statistics thoughtfully. This can be hard to do, precisely because so many people in our society treat statistics as fetishes. We might call this the mind-set of the Awestruck -- the people who don't think critically, who act as though statistics have magical powers. The awestruck know they don't always understand the statistics they hear, but this doesn't bother them. After all, who can expect to understand magical numbers? The reverential fatalism of the awestruck is not thoughtful -- it is a way of avoiding thought. We need a different approach.

But can't you prove anything with statistics? It depends on what "prove" means. If we want to know, say, how many children are "gunned down" each year, we can't simply guess -- pluck a number from thin air: 100, 1,000, 10,000, 35 trillion, whatever. Obviously, there's no reason to consider an arbitrary guess "proof" of anything. However, it might be possible for someone -- using records kept by police departments or hospital emergency rooms or coroners -- to keep track of children who have been shot; compiling careful, complete records might give us a fairly accurate idea of the number of gunned-down children. If that number seems accurate enough, we might consider it very strong evidence -- or proof.