In Bayes' Theorem, the probability of something occurring is based on probabilities of other parameters of the problem. Put simply, using the theorem builds on prior knowledge of the problem domain to update a prediction. This became very popular because, in the real world, there is much uncertainty and Bayes Theorem provides a way of modeling that uncertainty through probability (e.g. machine learning).
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Extrapolating from past data points is not an explanation. Building your confidence that something that will happen—like Bayes Theorem—is useful for descrete, observable problems, but fails to reveal the truth. It’s the equivalent of saying “because it’s always been that way” which is a flawed way of reasoning about the world.