Simulating the central limit theorem

Marshall A. Taylor ()
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Marshall A. Taylor: University of Notre Dame

Stata Journal, 2018, vol. 18, issue 2, 345-356

Abstract: Understanding the central limit theorem is crucial for comprehend- ing parametric inferential statistics. Despite this, undergraduate and graduate students alike often struggle with grasping how the theorem works and why re- searchers rely on its properties to draw inferences from a single unbiased random sample. In this article, I outline a new command, sdist, that can be used to simulate the central limit theorem by generating a matrix of randomly generated normal or nonnormal variables and comparing the true sampling distribution stan- dard deviation with the standard error from the first randomly generated sample. The user also has the option of plotting the empirical sampling distribution of sample means, the first random variable distribution, and a stacked visualization of the two distributions. Copyright 2018 by StataCorp LP.

Keywords: sdist; central limit theorem; simulation; runiform(); teaching (search for similar items in EconPapers)
Date: 2018
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