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The Central Limit Theorem explains a lot about sampling in statistics. How do we know what size of sample (n) to use and still be confident that the sample is representative of the population?
The Central Limit Theorem proposes that when we sample a population the greater the sample size will result in the mean of the samples being increasingly normally distributed. It will also reduce the spread of data (standard deviation).
In this spreadsheet exercise we have a population of 990 random numbers. we then test the Central Limit Theorem by charting a distribution curve for sample sizes of n=4, n=10 and n=30 to compare the difference.