Knowing What To Count

John Allen Paulos reminds us that being able to count isn’t enough, we have to know what to count.

Medical researchers face similar problems when it comes to measuring effectiveness. Consider the temptation to use the five-year survival rate as the primary measure of a treatment for a particular disease. This seems quite reasonable, and yet it’s possible for the five-year survival rate for a disease in one region to be 100 percent and in a second region to be 0 percent, even if the latter region has an equally effective and cheaper approach. 

 This is an extreme and hypothetical situation, but it has real-world analogues. Suppose that whenever people contract the disease, they always get it in their mid-60s and live to the age of 75. In the first region, an early screening program detects such people in their 60s. Because these people live to age 75, the five-year survival rate is 100 percent. People in the second region are not screened and thus do not receive their diagnoses until symptoms develop in their early 70s, but they, too, die at 75, so their five-year survival rate is 0 percent. The laissez-faire approach thus yields the same results as the universal screening program, yet if five-year survival were the criterion for effectiveness, universal screening would be deemed the best practice.

Remembering lessons like this should help keep us more humble in debating public policy whenever we think the facts are on our side. It’s not that we can’t learn anything through statistics or that statistics are “lies” but 

No method of measuring a societal phenomenon satisfying certain minimal conditions exists that can’t be second-guessed, deconstructed, cheated, rejected or replaced. This doesn’t mean we shouldn’t be counting — but it does mean we should do so with as much care and wisdom as we can muster. 

(h/t Freakonomics)

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