Over the past several years I’ve actively tried to manage my collaborators’ expectations regarding results of very standard biostat techniques. I have occasionally experienced that some then scale the importance of the statistician in relation to the stat technique.
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I believe this then opens the door for other practitioners of data science who are currently more optimistic about their tools (read: machine learning) to come in and market their techniques as the solution.
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'Adjusting for' is a vehicle for zooming in on the appropriate sub-population(s) for our inferences. When comparing sub-populations in terms of an effect, it helps to ensure comparability w.r. to all the factors we 'adjusted for' so we can better isolate the effect of interest.
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A forlorn battle if ever there was one.
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the narcism of small differences
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Why not just say “conditional on” which only implies what we expect to see in Y given that *we have seen* X=x and doesn’t imply in either stats jargon or everyday language anything more than that.
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