A human rights statistician finds truth in numbers
Whether gazing at a computer or into the eyes of a former dictator, numbers cruncher Patrick Ball is on the front lines of justice.
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He combined those scrungy papers, one for nearly every family that crossed the border, with crossing records kept by several international organizations; later, he brought in data from 11 sources on civilian deaths in the province. He analyzed the two separately, using one method for patterns of migration and another for mortality. There were three plausible causes for civilian flight and death – violence by the Kosovo Liberation Army (KLA) or the Yugoslav forces, or bombings of Serb targets by NATO – and he wanted to know which the numbers pointed to.Skip to next paragraph
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Like all statisticians, Ball began with the most basic hypothesis: In looking for a common cause, he is already wrong. Statistics begins with an original assumption – that everything is random – and discards it only when the data suggest otherwise. In Ball's case, they did: He found patterns in the mass movement of refugees strong enough to suggest that more than ordinary wartime chaos was at work. At the same time, the relationship between migration and NATO or KLA actions was so weak that he knew neither was the cause.
Statisticians have a language for description without interpretation. When the analysis showed the movements were neither random nor likely to follow NATO or KLA activities, Ball wrote: "The migration patterns of Kosovar Albanians are consistent with the hypothesis that there was a coordinated and organized effort to drive them from their homes." In layman's terms, the data suggested ethnic cleansing. In fact, the migration patterns matched killing patterns "so unbelievably perfectly" that he concluded that the two situations might be explained by the same external influence.
But this is where statistics, a science of elimination, cedes to lawyers, human rights practitioners, and historians. Observing a "consistent hypothesis" isn't the same as naming a cause. "When we're looking at data, it's what we're able to observe. That's not the same as what is true."
In the end, Ball can't say what did happen; he can only estimate what probably didn't. But even this reveals something bigger about the nature of truth: At a micro level, it seems to change, from town to town or person to person.
In Peru, Ball's team estimated the dead or disappeared in that nation's terrorist war in the 1980s to be twice as high as the estimate made by a human rights commission in Lima. "They said, 'How did we get it so wrong?' " he recalls. "Your risk of being killed ... up in the Andean highlands was 400 times greater than your risk of being killed in Lima ... [where] you feel like you're in the war, but ... a completely different war than the people up in the mountains."
That's why Ball finds all the painstaking work he puts into the macro picture of things worth it. In country after country, he has watched people "try to ... make their suffering have meaning in some bigger story," Ball says. He tries to ground that exercise in what he believes divides painful history from potentially destructive mythologies of violence: "Some kind of empirical truth."
But even so devoted a numbers guy knows graphs don't tell the whole story. "Statistics define the limits of what's plausible and what's not plausible," he says. "Statistics do not tell us how it felt to be there."
In 2000, just after a Kosovo newspaper published his conclusions about migration, Ball was on a radio show. "Someone called in and said, 'I'm in your graph,'" he recalls. "The peak, right there, that's where I was. I could feel that wave.' "