Ever wonder why prescriptions emanating from scientific authority change so radically from year to year? Here's one of the reasons why.
Many explanations have been offered to make sense of the here-today-gone-tomorrow nature of medical wisdom — what we are advised with confidence one year is reversed the next — but the simplest one is that it is the natural rhythm of science. An observation leads to a hypothesis. The hypothesis (last year’s advice) is tested, and it fails this year’s test, which is always the most likely outcome in any scientific endeavor. There are, after all, an infinite number of wrong hypotheses for every right one, and so the odds are always against any particular hypothesis being true, no matter how obvious or vitally important it might seem.Read it here.
The operative word here is "seem." Ideologues of various stripes, from Nazis to Marxists today's Lefty Luddites as well as unscrupulous opportunists have sought to claim for themselves the imprimatur of scientific authority. But what honest scientists know is always in a state of flux. What we think we know today is very different from what we thought we knew ten years ago and quite different from what we will think we know a decade from now.
The article goes on to detail the immense problems associated with epidemiological studies -- the kind of study that lies behind many of the stories trumpeted in the news regarding how people should live their lives. In many cases these studies have resulted in misguided attempts to forge public health policies. But so problematic are these studies that:
Public officials are always looking for information to guide their feeble attempts at effective and beneficial governance. Since the progressive era many officials have naively relied on scientific authority as a guide. But that is a dangerous course. Whether the subject is public health, or environmental policy, or racial justice, or any other big thing to which governments have turned their attention, "science" has proven to be a false and inconstant guide time and again.
As John Bailar, an epidemiologist who is now at the National Academy of Science, once memorably phrased it, “The appropriate question is not whether there are uncertainties about epidemiologic data, rather, it is whether the uncertainties are so great that one cannot draw useful conclusions from the data.”
The article cited above suggests that in epidemiological studies the standards of evidence have been lowered so far, and the inferences from ambiguous results so grossly inflated that medical "science" as currently practiced is not only immensely wasteful but may well be harmful in many cases to the public's well being.
Read the whole article in that notorious right wing anti-science source, the New York Times [here].