A simple example is height. On average men are taller than women.
But most of the time making a men=tall, women=short simplification does not make sense. It makes more sense to provide multiple sizes for both women and men.
And if providing only a very limited selection of sizes (e.g. hospital clothing) it makes sense to provide different unisex sizes rather than one for men and one for women.
And while we’re busy being tired, I’m really tired of no research by anybody (so far as I know) about keeping reactions to ideas one has about group differences in proportion to what one actually knows instead of exaggerating the size or extent of the differences.
It took rather a lot of hammering to get to the idea of atypical women.
I agree. I’m also tired of “Hey look! There’s overlap between the distributions, so let’s pretend the difference doesn’t matter!”
A simple example is height. On average men are taller than women.
But most of the time making a men=tall, women=short simplification does not make sense. It makes more sense to provide multiple sizes for both women and men.
And if providing only a very limited selection of sizes (e.g. hospital clothing) it makes sense to provide different unisex sizes rather than one for men and one for women.
And while we’re busy being tired, I’m really tired of no research by anybody (so far as I know) about keeping reactions to ideas one has about group differences in proportion to what one actually knows instead of exaggerating the size or extent of the differences.
It took rather a lot of hammering to get to the idea of atypical women.