Simpson's Paradox
We're familiar with how combining data can obscure details, but less aware of how it can create entirely new—and false—effects. This statistical sleight of hand, exemplified by Simpson's paradox, occurs when individual data sets clearly favor one conclusion, yet when combined, mysteriously support the opposite. Statistician Edward Simpson documented how subsamples might consistently show A outperforming B, while the amalgamated data inexplicably shows B superior to A. The danger is that these artificial patterns can lead to false accusations or misguided decisions based on what appears to be solid evidence. This mathematical quirk reminds us that data, even when accurately collected, can tell stories that aren't actually true when viewed from different angles. The numbers themselves may be correct, but the narrative they suggest might be entirely fictional.