Weak consistency is a property of an estimator indicating that it converges in probability to the true value of the parameter being estimated as the sample size increases. This concept connects closely to other key properties such as unbiasedness and efficiency, as weakly consistent estimators can still provide valuable insights, even if they are not unbiased. Understanding weak consistency is essential for evaluating how well an estimator performs when faced with large datasets.
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