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The Central Limit Theorem (CLT) states that, given a sufficiently large number of independent and identically distributed random variables, their mean will be approximately normally distributed, regardless of the original distribution of these variables.

This is important because it allows the use of the normal distribution for estimation and analysis of data in various tasks, even if the original data are not normal. In machine learning and statistics, the CLT justifies the application of methods based on the normal distribution for constructing confidence intervals, hypothesis testing, and other analytical tasks.