The replies to this show that it isn't just language models which struggle with the difference between correlation and causality! Here's the (logically valid b

The replies to this show that it isn't just language models which struggle with the difference between correlation and causality! Here's the (logically valid but with fictional soundness) example I came up with: > The Elbonian people (A) are disproportionately tall (B). > Tall people (B) are disproportionately successful at basketball (C). > Elbonians (A) are disproportionately unsuccessful at basketball (¬C), because they don't play it. Edit: in context, I consider a negative correlation to adequately demonstrate the principle, which is that it is invalid to apply implication across a pair of correlations. But if you would prefer, we can substitute this third clause: > Elbonians (A) are precisely as successful at basketball as the rest of the general population, because despite their natural height, they have no cultural tradition of play, and therefore, tend not to do so. Your call.