Don’t trust us. Which is to say, the press isn’t always the most reliable transmitter of information about well-being. But thanks to changes in funding and new publishing models, you can now bypass us altogether and read the actual research papers yourself. The problem is, that’s harder than it sounds. Reading an original paper isn’t the same as understanding it. Science journalists try to help on that front, but as with any filter, important bits can get lost in translation. So you need to explore a few critical elements—of the study or the media coverage about it—to determine whether it contains life-changing advice or something best deposited behind the couch in the dentist’s office. And what are those elements? Glad you asked.

Causation vs. correlation

How do you know if a study’s results answer the question it set out to ask? Sometimes an outcome is just a coincidence—there’s a correlation but no causation. Meta-analyses pool the results of smaller studies and filter signal from that kind of noise.

How do you know if a study’s results answer the question it set out to ask? Sometimes an outcome is just a coincidence—there’s a correlation but no causation. Meta-analyses pool the results of smaller studies and filter signal from that kind of noise. True size of the effect

Watch out for weasely language—a “threefold increase” might only be a shift from 1 percent to 3 percent. One recent paper reported that women’s mortality risk rose 133 percent. That sounds scary, but the elevated mortality rate was still just 1.9 percent.

Watch out for weasely language—a “threefold increase” might only be a shift from 1 percent to 3 percent. One recent paper reported that women’s mortality risk rose 133 percent. That sounds scary, but the elevated mortality rate was still just 1.9 percent. Statistical power

Look at two key factors, the n and the p. The n is the number of subjects used in the study. Multifaceted experiments typically have fewer subjects than simple surveys. Genetics studies need a big n. The p value lets you know whether the result is “statistically significant”—it’s the probability of something occurring by chance alone. You want to see a p of less than 0.05. (Results can be statistically significant and still only show correlation, or have confounding factors.)

Look at two key factors, the n and the p. The n is the number of subjects used in the study. Multifaceted experiments typically have fewer subjects than simple surveys. Genetics studies need a big n. The p value lets you know whether the result is “statistically significant”—it’s the probability of something occurring by chance alone. You want to see a p of less than 0.05. (Results can be statistically significant and still only show correlation, or have confounding factors.) Conflicts of interest

Most journals now note this as a matter of policy. Was the company making the drug or product associated with the laboratory that did the study? Are any of the authors trying to sell a product? For example, the authors of a study exploring the effectiveness of “brain training” techniques on cognitive enhancement worked for the company that developed (and sold) those techniques. They disclosed this, but that’s still a red flag.



