Food science has a huge statistics problem. The solution, for now? Stop treating new nutrition studies like they contain the truth.

This article gets pretty technical in the statistical flaws in nutrition research. My advice for people who care about eating healthy and who can afford it simply eat food that is have little processing as possible. If it is in a box, or frozen as a pre-made meal, it likely is less healthy for you than the natural food. If you can't afford it, then it doesn't really matter.

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 bull$#@! 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.


Let’s look at three recent reports of scientific findings about diet:


  1. Fifty grams of prunes a day prevents the loss of bone mineral density in elderly women with osteopenia
  2. Forty-eight grams of dark chocolate modulates your brainwaves for the better.
  3. Feeding infants puréed pork causes them to put on more body length than feeding them dairy.
They’ve all been peer-reviewed. All the findings have been declared to be statistically significant. And they all imply a clear cause-and-effect between a common food and a health outcome. And yet we know that there’s a good chance that at least one of them—and maybe even all three—will subsequently be proven to be false. So which ones does it make the most sense to ignore?
Hint, only the 3rd study contains significant proof; although it does not say whether it matters if infants put on greater body length.