Running head: CENTRAL LIMIT THEOREM 1 Central Limit Theorem Name: Institution: Course name, code & title: Date: 2 The central limit theorem is highly significant in inferential statistics. When summarized it can be paraphrased to mean the following: that as the size of the sample increases regardless of the sample number in a random sample, the following three things happen. Firstly, the sampling distribution becomes more centered to the mean of the given population. Secondly, the sampling variability becomes lesser than that of the selected population. Lastly, the sampling distribution becomes more and more in likeness to the normal distribution of the population under study. (Charpentier) Skewness is an indication of variability between the sample distribution and the normal distribution in the population under study. Therefore for heavily skewed populations, the theory can be app ...
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