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Instead of well-run experiments and real evidence, many supposed rules are based on opinion, aesthetic judgments, and incomplete or oversimplified studies. In this Data Science Central webinar, I will walk you through a number of things that we thought we knew, but that on closer inspection turned out to be wrong – it turned out that we didn’t know them, after all. Knowing what we know, what we don’t know, and what we merely assume, is important so we don’t rely on supposed rules that are not actually based on evidence. To figure out which is which, we have to keep asking a simple question: how do we know that?