Asymptotic normality refers to the property of a statistical estimator whereby, as the sample size increases, the distribution of the estimator approaches a normal distribution. This concept is significant because it enables the use of normal distribution-based methods for inference, even when the original data is not normally distributed, as long as the sample size is large enough. This characteristic is particularly relevant in maximum likelihood estimation, where estimators derived from large samples can be approximated by normal distributions to simplify statistical analysis.
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