Almost 40% of peer-reviewed dietary research turns out to be wrong. Here’s why
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Food science has a huge statistics problem. The solution, for now? Stop treating new nutrition studies like they contain the truth.
May 15th, 2018
by Patrick Clinton COMMENTARY
There’s a reason everyone’s confused about whether coffee causes cancer, or whether butter’s good for you or bad. Food research has some big problems, as we’ve discussed here and here: questionable data, untrustworthy results, and pervasive bias (and not just on the part of Big Food). There’s reason to hope that scientists and academic journals will clean up their acts, and that journalists will refine their bullshit detectors and stop writing breathlessly about new nutrition “discoveries” that are anything but. Until that happens, though, we all need to get better at filtering for ourselves.
A pair of recent articles coming out of the statistical community offers a terrific tool for doing just that—not a long-term fix, but a little bit of much-needed protection while we wait for something better. To understand it, though, we’re going to have to dip our toes into some chilly mathematical waters. Stick with me. It won’t be too bad.