Scientific Proof: Numbers Are Unreliable

In a fascinating The New Yorker article I hope to read eventually, neuroscientist and frequent Radiolab-rat Jonah Lehrer proposes an elegant answer to the mysterious conundrum of the Decline Effect: All data is meaningless.

He may or may not actually have said this. It's a really long article. But it's probably what he meant, so I'll just go ahead and quote him as having said it. And Mr. Lehrer then goes on to opine that numbers indeed have a mind of their own and are capable of changing themselves at will in order to confuse scientists.

This is, as you know, what I've been saying for decades.

The Decline Effect - the primary topic of Lehrer's piece - is a phenomenon observed in many of the sciences by which a large effect measured in an initial study tends to decline as the subject is tested in follow-up studies. This is particularly evident in the medical sciences, manifesting as a stronger placebo effect when trialing new drugs. It is patently obvious to even the dimmest patent clerk what is happening here: the numbers are changing themselves based on their own personal whims and fancies, thus rendering all data invalidable.

Of course, card-carrying number-sympathizers like Steven Novella are quick to dismiss these obvious cases of Divide Intervention as "regression to the mean" or "the self-correcting nature of science."

Balderdash, I say! Science cannot self-correct if the numbers are faulty. And the numbers, evidently, are indeed faulty.

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