MGCR 271 Lecture Notes - Lecture 9: Central Limit Theorem, Sampling Distribution, Standard Deviation
3 Oct 2016
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Document Summary
If all possible samples of size n were randomly drawn from the same population, a collection of sample means would result as a random variable with its own mean and standard deviation. The probability distribution of these sample means is called the sampling distribution of . Assume that 500 samples of size 5 are randomly selected from a uniform population ranging from. The population mean is 20 and the population variance is 0. 0833. However, any one value of the sample mean will probably not equal the population mean. Mean variability is always less than individual variability. No matter what continuous distribution applies to the population, the sampling distribution of the sample mean is always approximately normal for sufficiently large sample sizes. The sample mean is approximately normal regardless of the underlying population, provided that the sample size is sufficiently large (n 30).
