Asymptotic normality refers to the property that, as the sample size increases, the distribution of a sequence of random variables approaches a normal distribution. This concept is closely tied to the Central Limit Theorem, which states that the sum (or average) of a large number of independent and identically distributed random variables will tend to be normally distributed, regardless of the original distribution of the variables. This principle is fundamental in statistics and helps in making inferences about populations based on sample data.
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