Lies, Damned Lies, and People Too Lazy to See the Difference
David Simon, an author, journalist, and a writer/producer, said to Bill Moyers:
One of the themes of The Wire really was that statistics will always lie. Statistics can be made to say anything.
You show me anything that depicts institutional progress in America: school test scores, crime stats, arrest reports, anything that a politician can run on, anything that somebody can get a promotion on, and as soon as you invent that statistical category, fifty people in that institution will be at work trying to figure out a way to make it look as if progress is actually occurring when actually no progress is.
I mean, our entire economic structure fell behind the idea that these mortgage-backed securities were actually valuable, and they had absolutely no value. They were toxic. And yet they were being traded and being hurled about, because somebody could make some short-term profit. In the same way that a police commissioner or a deputy commissioner can get promoted, and a major can become a colonel, and an assistant school superintendent can become a school superintendent, if they make it look like the kids are learning and that they’re solving crime. That was a front-row seat for me as a reporter, getting to figure out how once they got done with them the crime stats actually didn’t represent anything.
I agree with Simon that politicians and many others use statistics to lie, but I strongly disagree with the sentiment that “statistics will always lie.” Statistics can be and often are very true and extremely useful. It’s people who lie. They use statistics to lie in the same way they use any other fact, true or false. Correct statistics can often be used to out the lies–but when people believe that statistics as a whole is suspect, they will just as often use that as an excuse not to listen to the truth. They’ll believe the lie because they want to.
One reason statistics are often used is because they easy to express, but a more important reason is that they imply careful research was done to produce them, therefore giving a stronger sense of authority. It’s the same reason why interested parties will pay for fake “scientific” studies, like tobacco companies often do–because research carries weight and it’s easier for people to believe in research results.
Sometimes statistics are just made up, but more often they are the result of some kind of study or poll. Often the untruth lies in the intent of the study, but even when the study is completely legitimate, telling only specific results in the absence of a complete context–in essence, the half-truth.
An excellent example is the common right-wing lie that during the Reagan years, we cut taxes and doubled revenue. That’s a regular fact and a statistical fact. Both are true. However, they are used to present a completely false impression, that tax cuts during the Reagan era caused a doubling of revenue, thus proving correct the “trickle-down” theory. The lie comes from the misuse of the facts. Yes, it is true, we cut some taxes, but we raised others more, and in the end had a cumulative tax hike. And yes, revenues roughly doubled, but that figure fails to take inflation into account, something which distorts any claimed effect. The true reading of the facts tells us that taxes were raised slightly under Reagan and revenues increased by a few dozen percent–obviously not supporting trickle-down at all.
In this example, we see both statistical and non-statistical data used to lie–but that doesn’t mean that both types of facts will “always” lie. Discerning accurate and honest application of statistics is a necessary skill for any consumer of information.
Simon, in his broad dismissal of statistics as a whole, is being intellectually lazy–the same fault which causes people to believe bad statistics in the first place. People tend to take facts at face value and are too lazy to think critically, to question the facts they are presented, to check them or to apply logic to them, which would allow them to see past the lies.