|
Abstract:
|
We can better understand the Central Limit Theorem by visualizing the sampling distributions of sample means of different sample sizes from different underlying population distributions. Our examples include populations with negative skew, positive skew and high kurtosis distributions. Through these examples we illustrate the properties of the Central Limit Theorem. Using graphs, we show that the sampling distribution of sample means approaches a normal distribution, regardless of the underlying population distribution. Using tables, we show that the sampling distribution mean is equal to the population mean, and the sampling distribution standard error is equal to the population standard deviation divided by square-root of the sample size.
|
