I’m behind the curve a bit here, but I’ve seen and heard a bunch of
people making really sleazy arguments about the current financial stimulus
package working its way through congress, and those arguments are a perfect
example of one of the classic ways of abusing statistics. I keep mentioning metric errors – this is another kind of metric error. The difference between this and some of the other examples that I’ve shown is that this is deliberately dishonest – that is, instead of accidentally using the wrong metric to get a wrong answer, in this case, we’ve got someone deliberately taking one metric, and pretending that it’s an entirely different metric in order to produce a desired result.
As I said, this case involves the current financial stimulus package that’s working its way through congress. I want to put politics aside here: when it comes to things like this financial stimulus, there’s plenty of room for disagreement.
Economic crises like the one we’re dealing with right now are really uncharted territory – they’re very rare, and the ones that we have records of have each had enough unique properties that we don’t have a very good collection of evidence
to use to draw solid conclusions about recoveries from them work. This isn’t like
physics, where we tend to have tons and tons of data from repeatable experiments; we’re looking at a realm where there are a lot of reasonable theories, and there isn’t enough evidence to say, conclusively, which (if any) of them is correct. There are multiple good-faith arguments that propose vastly different ways of trying
to dig us out of this disastrous hole that we’re currently stuck in.
Of course, it’s also possible to argue in bad faith, by
creating phony arguments. And that’s the subject of this post: a bad-faith
argument that presents real statistics in misleading ways.