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4.4 The Central Limit Theorem

The Central Limit Theorem (Not Required)

  • The Central Limit Theorem (CLT) states that, in many situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution even if the original variables themselves are not normally distributed.
  • To explain in math, suppose that X=\frac{1}{n}\left(X_{1}+X_{2}+X_{3}+\ldots+X_{n}\right), then we can assume X \sim \color{red}N\color{black}\left(\mu, \frac{\sigma^{2}}{n}\right). The most important part here is N. as for the mean of variance of X, it can be calculated without the usage of any approximation/assumption (shown below).
  • We can find the mean and variance of X as below:
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