Central limit theorem
The central limit theorem is a fundamental result in probability theory and statistics that states that the sampling distribution of the mean of a large number of independent and identically distributed random variables will approximate a normal distribution, regardless of the shape of the original distribution. This theorem is of great importance in many areas, as it allows for the use of normal approximation and the application of statistical techniques in a wide range of practical situations.
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This theorem is of great importance in many areas, as it allows for the use of normal approximation and the application of statistical techniques in a wide range of practical situations. The theorem has numerous applications, such as estimating population means from sample means, hypothesis testing, confidence interval estimation, and understanding the behavior of random processes.