Watch for p in the Kool-Aid!

One "Johannes" Bohannon describes how he, "fooled millions into thinking chocolate helps weight loss". He fibs only a little, helped as he was by an army of scientifically illiterate -- and incredibly lazy -- "journalists".

I am Johannes Bohannon, Ph.D. Well, actually my name is John, and I'm a journalist. I do have a Ph.D., but it's in the molecular biology of bacteria, not humans. The Institute of Diet and Health? That's nothing more than a website.

Other than those fibs, the study was 100 percent authentic. My colleagues and I recruited actual human subjects in Germany. We ran an actual clinical trial, with subjects randomly assigned to different diet regimes. And the statistically significant benefits of chocolate that we reported are based on the actual data. It was, in fact, a fairly typical study for the field of diet research. Which is to say: It was terrible science. The results are meaningless, and the health claims that the media blasted out to millions of people around the world are utterly unfounded.

Among his tricks was the misuse of statistics:

... We didn't know exactly what would pan out -- the headline could have been that chocolate improves sleep or lowers blood pressure -- but we knew our chances of getting at least one "statistically significant" result were pretty good.

Whenever you hear that phrase, it means that some result has a small p value. The letter p seems to have totemic power, but it's just a way to gauge the signal-to-noise ratio in the data. The conventional cutoff for being "significant" is 0.05, which means that there is just a 5 percent chance that your result is a random fluctuation. The more lottery tickets, the better your chances of getting a false positive. So how many tickets do you need to buy?

...

With our 18 measurements, we had a 60% chance of getting some "significant" result with p < 0.05. (The measurements weren't independent, so it could be even higher.) The game was stacked in our favor.

It's called p-hacking...

There has been quite a bit of soul-searching in the scientific community regarding statistical methods lately. I am not sure if Bohannon's work helped lead to -- or was inspired by -- this, but I am glad to see that there is at least some backlash against the misuse of statistics in reporting about science.

-- CAV