Thoughts on @bengoldacre’s article this morning: I think Ben makes a very good point in pointing out that unemployment statistics are subject to sampling error, and that in many months, the change in unemployment is not statistically distinguishable for this reason.
However, I think Ben somewhat overstates this particular point. It’s not right to say, as he does in his final paragraph, that these statistics “tell us nothing”. We can’t dismiss all data that fails to pass the (entirely arbitrary) test of statistical significance. Non-statistically significant data contribute to our knowledge, but we shouldn’t put too much weight on them. (Ben knows this, of course: he pulled back a bit from this in a tweet to me, saying that what he really wanted was for economics reporters to acknowledge the lack of statistical significance, which would indeed be nice to see.)
But I liked Ben's final point, and he could have made more of it. There’s a lot of economic data out there that is noisy but for which sampling error bars are not appropriate. GDP data, for instance, is initially reported based on partial data (and not a random sample) and we gradually close in on a view of what GDP actually was. Sometimes we revise our views dramatically: Greece’s GDP (actual GDP, not GDP growth rate) jumped suddenly by 25 per cent late in 2006 thanks to a statistical revision, although I’ll admit Greece’s data is hardly the best in the world.
And what of the data that get far more airtime than every other economic variable put together – the movements of the stock markets? There is no sampling error here at all: the reporters really are able to tell us, minute by minute if they wish to, exactly what the stock markets are doing. As a consequence they do exactly that, producing reports that are full of sound and fury, signifying... well not nothing, but not a lot either. Stock market movements are extremely noisy, big money bets on the future of the economy. They can be reported with absolute precision. That doesn’t mean they should be.
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