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Pattern Recognition, Not Analysis: LLMs and the Correlation vs. Causation Trap In this reel, Dana DiTomaso breaks down why relying blindly on AI for data analysis can backfire if you don't understand how Large Language Models (LLMs) actually work. She explains that LLMs are built for pattern recognition, not true analysis, and they famously struggle with separating correlation from causation. Using a witty example about the historical data of left-handedness, Dana highlights how AI completely la
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There's a huge difference between correlation and causation and a lot of doctoral students confuse the one with the other. A baseball player eats chicken before every game and plays well, so he thinks eating chicken causes better performance. But that's not an experimental design, that's superstition. He's confusing
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