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In my work around weight science and healthcare, I see a lot of confusion about, and misuse of, statistics. Today I thought I would point out the three of the most common issues that I experience.

Sure, intentional weight loss fails 95% of the time, you just have to keep trying until you’re in the 5%.

I know not everyone took statistics, but I did, so let me assure you that this isn’t how statistics work on the most basic level (remember that this is the “logic” that many people use when playing the lottery.) in fact, weight loss is worse than the lottery in this respect because repeated attempts can actually have decreasing odds of success. The body responds to weight loss attempts by changing physiologically to become a weight-gaining, weight-maintaining machine, which it continues to do even after the diet ends. This can make repeated attempts even less likely to result in significant, long-term weight loss. Moreover, many people regain more than they lost, meaning that if they (or their healthcare provider) had a specific weight/BMI in mind, they may end up farther from it than they started. Not to mention that “failure” (being clear that the diet failed the patient, and not the other way around) is not benign. Weight cycling (losing weight and then gaining it back) is linked to significant harm, including health issues that get blamed on being higher weight.

But It’s Statistically Significant

In the most simplified explanation, if a study result is “statistically significant,” it means that it’s more likely that the result was caused by the study intervention than by chance. So participants could have lost an average of one pound, but if it’s determined that it’s more likely that the one pound loss was due to the weight loss intervention being studied than by chance, then that one-pound loss is statistically significant.

There are a couple of ways that this goes wrong.

Sometimes people either think that “statistically significant” means “important” (or they hope that other people will think that’s what it means,) so they’ll say that a result in a study was “statistically significant” without mentioning that the actual effect (the amount of weight loss, for example) was very small (one might even say…insignificant.)

Something else that happens with weight science is that the conclusion of a study (which is often the only part that is not behind a paywall) will state that participants lost “a significant amount of weight” when what they really mean is that they lost a small amount of weight, but that the weight loss was statistically significant. Whether accidentally or on purpose, due to the colloquial meaning of significant this misleads people (including healthcare practitioners) to believe that the intervention was far more successful than it actually was. So the conclusion might say that subjects lost a significant amount of weight when, if you get behind the paywall and dig into the study, you’ll find that they lost 2.9% of their body weight (and often, had already started regaining it when the study ended.)

Percent increase of complication risk vs percent of complication risk

Many healthcare procedures have risks of complications. Typically (and, again, this is a simplified explanation) the