Asymptotic Normality

Asymptotic normality refers to the idea that, as we gather more and more data, the distribution of a certain statistical estimate (like an average) tends to resemble a normal (bell-shaped) curve. This means that with large enough samples, we can predict how the estimate behaves and make reliable inferences, even if the original data are not normally distributed. Essentially, it’s a concept that assures us large samples make complex data easier to analyze by approximating familiar, well-understood patterns.