Impact of Sample Size on Approximating the Normal Distribution

Authors

Paul Savory, University of Nebraska at LincolnFollow

Date of this Version

2010

Comments

P. Savory (2010), “Sample Size Impact on Approximating the Normal Distribution”, Mathematica Software Demonstration, The Wolfram Demonstations Project. Available at: http://demonstrations.wolfram.com/SampleSizeImpactOnApproximatingTheNormalDistribution/

Abstract

As the number of samples from a normal probability distribution with a user-defined mean and user-defined variance increases, the more closely the sample probability distribution resembles the theoretical normal probability distribution. User controls allow for the specification of a mean, a variance, and the number of samples. The number of samples is randomly generated from the underlying Normal distribution with the given mean and variance. The Mathematica demonstration compares the sample probability distribution with the theoretical Normal distribution. As probability and statistical theory show us, as the number of samples increase for the given mean and variance, the more closely the sample probability distribution will resemble the theoretical distribution.