Not exactly Gaussian—that’s even theoretically impossible because a Gaussian has infinitely long tails—but approximately Gaussian. Bell-shaped, in other words.
An IQ test in which the scores are only normalized linearly is a worse approximation to a Gaussian distribution than one which is intentionally designed to give Gaussianly distributed scores.
Not exactly Gaussian—that’s even theoretically impossible because a Gaussian has infinitely long tails—but approximately Gaussian. Bell-shaped, in other words.
Fallacy of grey. Certain approximations are worse than others.
So in this particular example, which approximation is worse than which other approximation and by which metric?
An IQ test in which the scores are only normalized linearly is a worse approximation to a Gaussian distribution than one which is intentionally designed to give Gaussianly distributed scores.
Well, duh, but I don’t see the point.