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This video is part of a playlist on parameter estimation discussing complete statistics. Complete statistics are defined as a statistic following a distribution, either continuous or discrete, where a parameter theta exists in the parameter space omega. A statistic t is considered complete for a family of distributions if the expected value of any function of t equals zero for all theta, implying that the function g of t is also zero for all theta almost everywhere. Even if this condition is true except for one point, it still holds as that point has a probability of zero. An example is provided to show that the nth order statistic x_n is both sufficient and complete when x_i is from a certain distribution.
