From a methods perspective, wouldn't this be more of a statistical power issue (too small of sample size) than a random effect issue? Granted, we do a terrible job discussing statistical power.
Multimodal distributions are everywhere, and we are losing key insights by ignoring this. A classic example is the difference in response between men and women to a novel pharmaceutical.
It’s certainly not the case that scientists are not aware of this fact, but there seems to be a strong bias to arrange studies to fit into normal distributions by, for example, being selective about the sample population (test only on men, to avoid complicating variables). That makes pragmatic sense, but I wonder if it perpetuates an implicit bias for ignoring complexity.
For example, checking account balances are far from a normal distribution!
So although I look like a typical European Caucasian, my genetics are very untypical of that population. And this also explains my family history of heart diseases and mood disorders which are also non-typical of European Caucasians.
In most African populations this Polymorphism does not exist at all. And even in Europeans it is only about 12% of the population.
I agree, but then all these cheaper, easier studies are useless.