Watching from the sidelines, I’ve always wondered why everything in the life sciences seems to assume unimodal distributions (that is, typically a normal bell curve).
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.
It’s because statistical tests are based on the distribution of the statistic, not the data itself. If the central limit holds, this distribution will be a bell curve as you say
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.