In 1964, when the physicist Richard Feynman presented what would become a renowned series of lectures at Cornell University, he observed that it was a natural condition of scientists to be biased or prejudiced toward their beliefs. That bias, Feynman said, would ultimately make no difference, “because if your bias is wrong a perpetual accumulation of experiments will perpetually annoy you until they cannot be disregarded any longer.” They could be disregarded, he said, only if “you are absolutely sure ahead of time” what the answer must be.

In the case of Keys’s hypothesis, the annoying evidence was consistently disregarded from the beginning. Because the totality of evidence was defined as only those data that confirmed the hypothesis, Keys’s hypothesis would always appear monolithic. Annoying observations could not force a reanalysis of the underlying assumptions, because each of those observations would be discarded immediately as being inconsistent with the totality of the evidence. This was a self-fulfilling phenomenon. It was unlikely, however, to lead to reliable knowledge about either the cause of heart disease or the routes to prevention. It did not mean the hypothesis was false, but its truth could never be established, either.

One other method can be employed to judge the validity of the hypotheses that dietary fat or saturated fat causes heart disease, and that cholesterol-lowering diets prevent it. This is a technique known as meta-analysis, viewed as a kind of last epidemiological resort in these kinds of medical and public-health controversies: if the existing studies give ambiguous results, the true size of a benefit or harm may be assessed by pooling the data from all the studies in such a way as to gain what’s known as statistical power. Meta-analysis is controversial in its own right. Investigators can choose, for instance, which studies to include in their meta-analysis, either consciously or subconsciously, based on which ones are most likely to give them the desired result.

For this reason, a collaboration of seventy-seven scientists from eleven countries founded the Cochrane Collaboration in 1993. The founders, led by Iain Chalmers of Oxford University, believed that meta-analyses could be so easily biased by researchers’ prejudices that they needed a standardized methodology to minimize the influence of such prejudice, and they needed a venue that would allow for the publication of impartial reviews. The Cochrane Collaboration methodology makes it effectively impossible for researchers to influence a meta-analysis by the criteria they use to include or exclude studies. Cochrane Collaboration reviews must include all studies that fit a prespecified set of criteria, and they must exclude all that don’t.