I agree with all of this. Outliers are really individuals in your sample that come from a different population than you intended to collect, not necessarily extreme values.
Solomon is getting at the part of my thought process that I forgot to include in the original post + added to the quote tweet- what to do instead to not have this issue that isn't endlessly removing data 1/2
ah sorry, lost that one! and it is based on Cook's D.
I see where ya'll are coming from that even if they are identified that doesn't mean it's necessary to remove them if they otherwise seem valid. Still have to shake off the "follow the rules automatically" stage of statistical learning I guess
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But the posterior predictive checks r fine so glmm iisnt necessary
maybe see if there is a random effect that might help?
I see where ya'll are coming from that even if they are identified that doesn't mean it's necessary to remove them if they otherwise seem valid. Still have to shake off the "follow the rules automatically" stage of statistical learning I guess