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I am a theoretical neuroscientist. I study models of brain circuits, precisely the sort of models that would be needed to try to reconstruct or emulate a functioning brain from a detailed knowledge of its structure. I don’t in principle see any reason that what I’ve described could not someday, in the very far future, be achieved (though it’s an active field of philosophical debate). But to accomplish this, these future scientists would need to know details of staggering complexity about the brain’s structure, details quite likely far beyond what any method today could preserve in a dead brain.

As the doctor Mark Tonelli has argued, distinct forms of knowledge can’t be judged by the same standards: what a patient prefers on the basis of personal experience; what a doctor thinks on the basis of clinical experience; and what clinical research has discovered — each of these is valuable in its own way. While scientists concur that randomized trials are ideal for evaluating the average effects of treatments, such precision isn’t necessary when the benefits are obvious or clear from other data.

Distrusting expertise makes it easy to confuse an absence of randomized evaluations with an absence of knowledge. And this leads to the false belief that knowledge of what works in social policy, education or fighting terrorism can come only from randomized evaluations. But by that logic (as a spoof scientific article claimed), we don’t know if parachutes really work because we have no randomized controlled trials of them.

Experiments, of course, are invaluable and have, in the past, shown the consensus opinion of experts to be wrong. But those who fetishize this methodology, as the flossing example shows, can also impair progress toward the truth. A strong demand for evidence is a good thing. But nurturing a more nuanced view of expertise should be part of that demand.