The Factorization Theorem states that a statistic is sufficient for a parameter if the likelihood function can be factored into two parts: one that depends only on the data and the parameter, and another that depends only on the data. This theorem provides a way to identify sufficient statistics and is fundamental in deriving properties of maximum likelihood estimators, helping in simplifying complex problems in statistical inference.
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