The key to this strategy is to bias the data seen by the public.
Holman and Bruner illustrate this point with a stark example.
Heart arrhythmias often precede heart attacks, so in the 1970s researchers began testing drugs intended to suppress arrhythmia. But while some researchers ran studies testing whether the drugs actually reduced mortality, others simply tested whether the drugs reduced arrhythmia.
The simplistic picture of how this influence works involves fraudulent science for sale: a shady industrial representative delivers a briefcase of cash to some scientist—who promptly publishes the desired results.
But in researching this topic, we found that more often representatives of industry are far more sophisticated, using subtle techniques that can shift scientific consensus and which are much more difficult to detect.
Representatives of industry are far more sophisticated, using subtle techniques that can shift scientific consensus and which are much more difficult to detect.
Even more pernicious is when industrial and political groups shape the body of evidence that is produced and published.
Selective sharing relies on the fact that all scientific evidence is probabilistic: Not everyone who smokes gets cancer, and not everyone who gets cancer smokes.
This means that some well-run studies, free from industry influence, will yield misleading results.
The sugar industry offers another version of the same problem.
As we saw, Big Sugar contacted and funded researchers who were already convinced of the dangers of fat, but not sugar.