Will. Not. Rise. To. Bait.

Someone sends me an email, “I don’t know what to do with this so I thought I would send it to you,” with a link to a university press release about a recently published research paper, full of silly statistical errors and signifying nothing.

I replied:

Can’t you just ignore this? Why give it any attention at all? There are millions of research papers published every year, right? Why play the publicists’ game? Let’s just leave this sort of thing for NPR, the Huffington Post, and other purveyors of junk science headlines.

22 thoughts on “Will. Not. Rise. To. Bait.

    • Daniel:

      Kanazawa is the least of it. Back when I was reading Kanazawa’s papers . . . well, there was something logical about the whole situation: Kanazawa was an intentionally provocative writer with a knack for getting papers published even though (or especially because) they had ridiculous claims backed up by erroneous statistics. But it was easy to think of him as a special case.

      The more recent world of psychology research is more disturbing because it’s not just headline-grabbing Kanazawas, it’s dozens (I guess hundreds, maybe thousands) of researchers all over the world using statistical errors to get headline-worthy claims.

      We could laugh at Kanazawa because his claims were so ridiculous. It’s harder to laugh when it’s a whole subfield of psychology, backed up by Harvard professors and the like. Then it’s time to cry.

  1. Out, out, brief study!
    It’s but a printed shadow, a poor paper,
    That struts and frets its hour upon the page,
    And then is seen no more. It is a tale
    Told by an idiot, full of silly stats errors,
    Signifying nothing.

  2. hahahahahahaha
    so happy that ain’t me.

    i’ll continue indulging in the perceived ‘village idiot’ persona.

    my phone rings, MAYBE, a few times a month.
    my inbox receives, MAYBE, one or two “work” emails a month.

    :))))))))

    • Not sure about Andrew, but usually what I care about is predictions, independent replications, and comparing various explanations. So some questions about:

      1) What previously proposed predictions has your study ruled out or not?
      2) What new predictions have your results lead you to make that we should see tested in the future?
      3) Did your work replicate the results, in whole or in part, those of any previous research?
      4) If there are plans for anyone to attempt replicating the current study, what difficulties may they face?
      5) What are alternative possible explanations for your results, how difficult will it be to distinguish between these in the future?

      The language may need to be spiced up to give it any wide appeal…

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