“The word representing something is not that real something. There is the representation and there is what someone is attempting to represent. If you confuse these two items people have the right to disregard you.”

And accuse you of not being really good at math – because in reality – you aren’t ;-)

]]>Examples from statistics:

Power: The statistical concept has morphed from the fairly well-defined concept of “ability to detect a difference of a specified amount” to things like “high power, medium power, low power” defined in terms of a standardized effect size.

Control: The use of the term “control group” in a randomized trial, or “control for this variable” in a regression often lead people to think that something stronger is happening (perhaps like “this switch controls this light”) than in reality is happening. (That’s why I try to use the terms “comparison group” and “try to account for this variable” instead — they are less likely to suggest the certainty that “control” often does.)

]]>I agree with everything you say in this post, except “a) incredibly good at math”. It is possible that they might in fact not be all that good at math. ]]>

Sonquist, J. A. and JN Morgan (1964). The detection of interaction effects: a report on a computer program for the selection of optimal combinations of explanatory variables. [Ann Arbor]: Survey Research Center, Institute for Social Research, University of Michigan.

]]>I get that impression because of the following conversation that took place after a seminar:

jrc: So I see you’ve done some work on the Age-Period-Cohort problem. I love that stuff, but it gives you a real headache after a while.

Author: We solved that problem.

jrc: (confused) But, like, I mean, you can’t solve it solve it, right? Like, it is a problem you have to get around but nothing changes the fact that cohort plus age equals period.

Author: No. We solve it. The optimal…

jrc: But wait I mean it must depend on the set of outcomes and the questions you are asking….

Author: No. We solved it. You…

And by that point I just so completely flummoxed that this person was standing there telling me that there was a way to perfectly disentangle three completely non-separable aspects of an observation that I couldn’t even focus on their “solution.” It was just the pure hubris of the whole thing: “Oh, that logically impossible to solve problem… we solved that perfectly, with logic.”

I think this is actually a nice example of a certain type of statistician – one who is a) incredibly good at math; and b) doesn’t really think about the world. Because you get the impression that they can’t separate out the two issues involved in solving a statistical problem: 1) set up a model of how the world is; 2) solve for some optimal solution given that world. They do 1, then promptly forget that 1 was a choice and a way to represent some aspect of the world but not the whole thing. Then they take (1) as how the world actually is and believe that, by doing (2), they’ve solved whatever problem they set up (1) to emulate. The problem is that the logic can be just fine, given the model they define in (1), but no one model of the world can ever be THE one model, and so the result of the logic is never a universal solution, just a local one. And this is how you get people saying they’ve solved the Age-Cohort-Period problem: they found a data generating process under which they can identify all three, and so it is solved.

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