Discussion about this post

User's avatar
Barbelo of the Pleroma's avatar

I am an old timer in stats in data, both academia and industry, and I completely agree. I think this dimension of bias is the least understood in science.

I used to explain it as an axiom: if a group publishes a finding, but their careers would be destroyed by finding anything contrary, then that paper contains zero informational value.

I have friends with faculty appointments who were told on day one, your job now is to bring in grant money to this university. Like a law partner who is supposed to make it rain. I don't know why anyone thinks someone with those incentives, will go against any headwinds in pursuit of truth. Some do anyway, but they are exceptional people.

And like you say, too few people understand that typical statistical methods only rule out chance to a certain degree. This always means if enough studies are run, false positives will appear. I remember reading about a drug that was finally approved after 33 trials! More evidence something is a placebo is not possible.

This is also why research is meaningless until there are independent direct replications. Basically everyone understands this since 2011 but no one does it, because, see axioms and incentives above.

Expand full comment
Σχολιαστής's avatar

I work at a working class setting where the bosses demand everyone wear a mask. Due to the nature of my job, I get to visit new such locations almost weekly. One thing I’ve noticed at all of these places is that absolutely no one ever wears a mask during breaks when only their co-workers can see them. The people know, and they know that the others know. One of the things I hate the most about these stupid mask mandates is that they require people to behave in a fundamentally dishonest way.

Expand full comment
73 more comments...

No posts