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The video discusses parameter estimation and focuses on complete statistics. A complete statistic, denoted by T, follows a distribution where the parameter theta belongs to the parameter space omega. For a statistic T to be complete for a family of distributions indexed by theta, the expected value of any function of T is zero implies that the function itself is equal to zero. Even if this is not true for only one point, it still holds since a point's probability is zero. An example shown is that the nth order statistic, x_n, is both sufficient and complete when x_i is from a given family of distributions.
