The Neyman-Fisher Factorization Theorem provides a criterion for determining when a statistic is sufficient for a parameter, by demonstrating that a probability distribution can be factorized into a product of two functions, one of which depends solely on the data and the other solely on the parameter. This theorem is fundamental in the field of statistics as it simplifies the process of identifying sufficient statistics, which are crucial for efficient data analysis and inference.