First off, got a chuckle from the bot check…

The story quoted new poll findings by a company called Aaru, representing them as research based on the feedback of American adults. But according to an editor’s note, the piece had to be “updated to note that Aaru is an AI simulation research firm.”

In other words, Axios had failed to disclose that it was citing alleged “polling data” that wasn’t drawn from human respondents at all. Instead, it was dreamed up by a large language model —yet the latest sign of every imaginable industry trying to leverage AI, even when doing so makes absolutely no sense.

This was/is a problem, but giving up on stats because bad stats exist, is like refusing to ever eat food again because someone got you to try a sardine and spinach chocolate cupcake one time.

In fact, the first, last, and most often brought up topic in graduate level statistical analysis isn’t about getting numbers, that’s easy. The hard part is finding the flaws in numbers, even in your own that proves yourself wrong.

The vast majority of people never learn that, or learn that bad stats have been a problem as long as stats has existed. Even making it thru peer review doesn’t always mean anything.

Like, every single time an article links to a study, do the due diligence and click, so what’s going, what the numbers really say, and search who funds them.

It’s not like you’ll even know what to look for at first, but if you never try you’ll never improve.