In his excellent book The Rise and Fall of Modern Medicine, Dr. James Le Fanu explaines that in the 1970s the old method of medical science -actual trial and error experimentation - came under attack with the introduction of the Social Theory of medicine. In describing its history, Dr. Le Fanu says:
"It is possible, however, to come to a reasoned judgement by examining its historical evolution, which takes us back first to 1976 when Thomas McKeown, professor of Social Medicine at Birmingham University, launched an assault on the prevailing view of the time that the enormous improvement in health in the preceeding 100 years had been brought about by the progress of medical science."
"'Medical science and its services are misdirected' he said. (P281) Mr. Le Fanu continues with:
"It cannot be sufficiently stressed what a radical departure this Social Theory of disease was from the preceding thirty years."
"But now here were distinguished doctors and scientists arguing that the future direction of medicine lay in a completely different direction: get people to change their diets, control pollution and eradicate poverty, and many diseases would evaporate like snow on a sunny day." (p284)
"It might sound almost too good to be true, but the Social Theory was enthusiastically taken up by many intelligent observers, as reflected in the BBC's prestigious Reith Lectures for 1980, given by a young lawyer, Ian Kennedy, committed to the 'unmasking of medicine.'" (p284-285)
In other words, these new scientists were saying in effect: "Come with us. We have a shortcut to truth."
In my judgement at least, that shortcut was the Social Theory coupled with epidemiology, also known as statistics.
This branch of mathematics was being encouraged to take the place of actual laboratory experiments. Instead of seeking proof that A caused disease B, all that is required is a correlation between A and B. If you can get enough other people to agree that the correlation exists, then you have a "consensus" and that in turn becomes sufficient evidence on which to base government policy (force). Thus the process of forcing reason out accelerates.
In his book Junk Science Judo, Steven J. Milloy writes:
"Statistics aren't science. They may be quantitative characterizations of observations. They may be estimates from mathematical models. In either case, statistics don't explain observations or validate models." (p70) He then quotes Dr. Bruce G. Charlton, M.D. of the University of Newcastle who, in his paper "Statistical Malpractice" wrote:
"There is a worrying trend in academic medicine which equates statistics with science, and sophistication in quantitative procedures with research excellence. The corollary of this trend is a tendency to look for answers to medical problems from people with expertise in mathematical manipulation and information technology, rather than from people with an understanding of disease and its causes."
Two paragraphs later he adds: "Science is concerned with causes but statistics is concerned with correlations..." (p74)
Statistics then, don't prove causation mainly because they can't, or to be more precise, they are not designed to. By discovering associations, statistics can point science to possible causes, but then science still has to step in and perform an experiment to prove or disprove causation.
But that's not what's happening in science today. Correlations are held up as evidence of causation. "Consensus" is hailed as proof. To be continued