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Confirmation math, also known as Bayesian inference, is a statistical method used to update the probability of a hypothesis as more evidence or data becomes available. It involves combining prior knowledge (prior probability) about the hypothesis with new evidence (likelihood) to calculate a posterior probability, which represents the updated belief in the hypothesis' truth given the new data. The formula for confirmation math is P(H|E) = [P(E|H)
P(H)] / P(E), where P(H|E) is the posterior probability of the hypothesis given the evidence, P(E|H) is the likelihood of observing the evidence if the hypothesis is true, P(H) is the prior probability of the hypothesis, and P(E) is the probability of the evidence under all possible hypotheses. This approach allows for a more nuanced and flexible way of reasoning under uncertainty, integrating both prior beliefs and new data to refine our understanding.