Central limit theorem

Central limit theorem is one of the most fundamental theorems in probability and statistics. The theorem states that sampling distribution of the mean of any independent random variables approaches normal as the sample size increases under certain conditions. Below I created a Shiny application to visualize central limit theorem in effect. Random samples are generated from a selected population distribution to visually assess the distribution of their means against the theoretical asymptotic normal distribution.

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