Morning coffee: null in genetics

coffee

Andrew Gelman sometimes writes that in genetics it might make sense to have a null hypothesis of zero effect, but in social science nothing is ever exactly zero (and interactions abound). I wonder whether that is actually true even for genetics. Think about pleiotropy. Be it universal or modular, I think the evidence still points in the direction that we should expect any genetic variant to affect lots of traits, albeit with often very small effects. And think of gene expression where genes always show lots of correlation structure: do we expect transcripts from the same cells to ever be independent of each other? It doesn’t seem to me that the null can be strictly true here. Most of these differences have to be too small for us to practically be able to model them, though — and maybe the small effects are so far below the detection limit that we can pretend that they could be zero. (Note: not trying to criticise anybody’s statistical method or view of effect sizes here, just thinking aloud about the ”no true null effect” argument.)

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