Maybe this is a GD, but I do hav two specific GQs. My apologies; I know this has come up before, but…
What specific errors, if any, did Murray and Herrnstein make in the “Bell Curve” with respect to their statistical analysis of the data in terms of the data, data sets, stat bias, group bias, or whatever? This is assuming assumptions about race are true.
Holding 1 steady and assuming they made no statistical errors, what errors did they make in terms of their assumptions around race and the nature of IQ heritability?
If anyone wants to DEFEND “The Bell Curve,” take it to GD I’d just like an answer to 1 and 2.
It’s a big question. Moderators, my apologies if this is in the wrong place, but I was looking for a specific answer(s) and figured I may as well start it here.
Here is an essay that Stephen Jay Gould wrote for The New Yorker that picks apart the assumptions, motivations, and methodologies of the studies conducted for The Bell Curve. It’s an interesting read, like most of Gould’s essays.
As I understand it, the biggest flaw was in sample selection. Most of the white individuals under study were selected from colleges, while most of the black individuals were selected from prisons. It should come as no surprise that the typical college student is smarter than the typical inmate, regardless of race.
There is an excellent book on essays critical of The Bell Curve (I think it’s called The Bell Curve Debate). It includes Stephen Jay Gould’s article and many others. What’s appalling is that the data was gathered from all over, from all sorts of tests that were not at all similar or compatible, so ALL the data hads to be fudged to put it into something like comparable form. When you torture data that far, you can’t really draw meaningful conclsions from it.
I think you’re referring to “The Bell Curve Wars”, edited by Steven Fraser (1995). All are critical to varying degrees - I recommend the articles by Gould and Nesbitt. They pick apart the underlying assumptions, methodology, and data used.
As I recall their errors were legion, including non-comparable data sets, assuming away problems with many flawed --even biased sources-- and inappropriate use of statistical tools (I seem to recall use of r where r2 was actually meant).
Assumption race is biologically valid.
Assumption IQ heritability is fully genetically based (as I recall).
Rather blows the whole game.
Then you have to get into the real meaning of IQ, and all the problems associated with measuring intelligence.
Terribly flawed with far too many assumptions. Hard not to conclude they were cooking the books.
I’d just like an answer to 1 and 2.