The Central Limit Theorem

The Central Limit Theorem is the cornerstone of inferential statistics.  In general, this theorem states that, if we draw many same size samples from a population, and plot the values of the means of those samples, the mean of this distribution will approximate the value of the population mean.  It is this powerful theorem that makes it possible to draw inferences about population parameters when we don’t know the nature of the population distribution.  

The following video provides a brief overview of the Central Limit Theorem and it is application in statistical analysis: