I’ve heard a lot about CocaCola, Marlboro and Oil companies “purchasing” or “commissioning” studies that only benefit them, and could barely pass as “true”.
How common is this and how does it work?
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You (and they) really don’t. What happens is more like this: What they do is write a fake paper like you might for a middle school test you didn’t study for (the teacher being the scientific review process).
The principle, to encourage students, has a board students can pin homework they’re proud of on. (These are the news companies).
A failing student ‘grades’ his own paper, giving himself a 100% without ever actually giving it to the teacher (he also makes up a fake teacher name to sign it with) or bothering to double-check his answers. He then goes and pins it on the board.
That’s basically how it goes. The companies literally write a fake research paper, sign it with a phony institution name, and go straight to the newspapers, not actually getting involved with the science at all.
It is relatively difficult to buy a credible study that’s actually false, because a credible study requires that you publish all the data, how you did the experiment, how you got the conclusion, and somebody else replicates it. If it’s false, you can’t actually do that so it’ll get caught eventually.
It’s relatively easy to publish a study that *looks true*, which isn’t the same thing. You can constrain the problem to a very narrow or unrealistic subset, or use unreasonable filters. You’ll get a technically correct conclusion that doesn’t really apply to the actual problem.
In both cases, this can be fodder for media reporting. The media will almost certainly not dive into the details and will just report the headline…this happens all the time and if you’re doing it to sway public opinion it can be really effective. The original “vaccines cause autism” study was an example of the former, the original cold fusion fiasco was an example of the latter.
Depends on the people or organization writing it. usually though, it’s not bought scientists you need to worry about, but rather built ones.
A lot of industries will create their own fake science organisations and get them to write papers in support of whatever. These papers should be regarded as fake rather than false, as little science actually went into them if any.
In general, you can make data say whatever you like, especially when it comes to make projections.
One of the most common methods would be called ‘p-hacking’ if it was done by a single researcher but is generally called ‘cherry-picking’ when done by outside agencies. Let’s say 100 different researchers study whether Coca-Cola consumption aids athletic performance.
Now, we’re pretty sure it doesn’t. Coca-Cola is just fizzy sugar water, after all. There should be no correlation between athletic performance and consuming it.
But if you have 100 different studies about it, *some* of them are going to show a correlation based on pure random chance. Just take those ones and ignore the rest.
You can also manipulate studies by selecting models that implicitly favor your point of view. If you want to prove the police are racist, you define ‘racist’ as ‘disparate outcomes’ and make no attempt to explain disparate outcomes by any other method. If you want to make solar panels look good, you emphasize ongoing costs in the early days of adoption and ignore the more expensive elements of life cycle cost. And so forth.
Essentially, there is so much subjectivity to the process that you can ‘prove’ anything you want.
Nor does peer review particularly help. Peer review is primarily a review of methods, not a critique of content. As long as your methodology is correct and your conclusions aren’t *too* ridiculous, it’ll pass peer review.
As a result, most ‘studies’ you read in the press really need to be treated with skepticism. They’re inevitably going to match the bias of either the researchers or the funders, especially when neither of those individuals have any vested interest in being right.
The studies that tend to be very accurate are ones where the researchers/funders suffer serious consequences for being wrong. If an oil company funds a study to determine where to drill for oil, it’s a good bet that study is going to be fairly accurate because they lose a lot of money if it’s wrong.
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