Numerical Analysis II
The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's original distribution. This theorem is crucial because it underpins many statistical methods, allowing for inference about population parameters based on sample statistics. It also plays a vital role in areas such as Monte Carlo integration and convergence theory, where understanding distributions is essential for accurate estimations and analysis.
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