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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
by tomrod 2y ago
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.
- Etheryte 2y agoThis effect would be even more pronounced in a larger sample size. Consider how small a fraction indigenous arctic populations are of the population as a whole. In other words, larger sample sizes would be even worse off in this particular occasion.
- FollowingTheDao 2y agoBut it is more complicated. I have Sami Heritage but it goes back to my great great grandparents. I did not know this until I started digging deeply into my ancestry, but I carry many of the polymorphisms from these people. 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.
- uniqueuid 2y agoThe best way to talk about this is IMO effect heterogeneity. Underlying that you have the causal DAG to consider, but that‘s (a) a lot of effort and (b) epistemologically difficult!
- FollowingTheDao 2y ago> but that‘s (a) a lot of effort and (b) epistemologically difficult! I agree, but then all these cheaper, easier studies are useless.
- FollowingTheDao 2y agoEven if you did a study with the whole planet there would be no statistical significance since the genetic variation in in FADS genes are still in the minority. (The majority of the world is warm and this is a cold weather/diet adaptation). In most African populations this Polymorphism does not exist at all. And even in Europeans it is only about 12% of the population.
- zukzuk 2y agoWatching 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.
- biophysboy 2y agoIt’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
- tomrod 2y agoAye, but there are cases where it doesn't hold. Lognormal and power law distributions are awfully similar in samples, but matters on the margin. For example, checking account balances are far from a normal distribution!