The Central Limit Theorem is a fundamental concept in probability theory and statistics that states that, under certain conditions, the sum or average of a large number of independent and identically distributed random variables will be approximately normally distributed, regardless of the underlying distribution of the individual random variables. It has many important applications, including hypothesis testing, confidence interval estimation, and statistical process control, but it also has limitations, including the assumptions of independence and a large sample size. Despite these limitations, the Central Limit Theorem is a powerful tool for analyzing and understanding data.

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