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The applets in this section of Statistical Java allow you to see how the Central Limit Theorem works. The main page gives the characteristics of five non-normal distributions (Bernoulli, Poisson, Exponential, U-shaped, and Uniform). Users then select one of the distributions and change the sample size to see how the distribution of the sample mean approaches normality. Users can also change the number of samples. To select between the different applets you can click on Statistical Theory, the Central Limit Theorem and then the Main Page. At the bottom of this page you can make your applet selection. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/

This resource contains a broad range of information concerning statistics. It is divided up into 18 chapters and also includes links to other resources pertaining to statistics.

This goal of this resource is to aid in the understanding of the relationship between statistics and the scientific method and how it applies to psychology and the behavioral sciences. The learner will learn how to read and understand the statistics presented in the professional literature and will learn how to calculate and communicate statistical information to others.

A pinch of probability is worth a pound of perhaps. A quote by American humorist and cartoonist James Thurber (1894 - 1961). The quote appeared in "Such a Phrase as Drifts Through Dreams," a short story in Thurber's last book, "Lanterns and Lances", Harper Publishing, 1961. The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

This applet allows you to explore the validity of confidence intervals on a proportion with various values for sample size (N) and population proportion (Pi). After you specify N, Pi, the level of confidence, and the number of simulations you wish to perform, the applet samples data according to your specification and computes a confidence interval for each simulation. The proportion of simulations for which the confidence interval contains Pi is recorded. If the method for constructing confidence intervals is valid, then about 95% of the 95% confidence intervals should contain Pi.

This simulation applet shows groups of confidence intervals for a given alpha based on a standard normal distribution. It shows how changes in alpha affect the proportion of confidence intervals that contain the mean. An article and an alternative source for this applet can be found at http://www.amstat.org/publications/jse/v6n3/applets/confidenceinterval.html.

This section of the Engineering Statistics Handbook gives the normal probability density function as well as the standard normal distribution equations. Example graphs of the distributions are shown and a justification of the Central Limit Theorem is included.

This tutorial takes the learner step-by-step in applying descriptive and inferential statistics using a real world situation.

This tutorial illustrates the basic principles of the Central Limit Theorem and enhances conceptual understand of why the Central Limit Theorem is important to inferential statistics.

This comprehensive collection of calculators provides users with resources for everything from introductory statistics to advanced statistical methods. Users can search by the following categories: Dictionaries, Courses with Calculators and Applets, Courses All Inclusive, and Statistics A-Z. Users can also search by the following statistical specialties: Agriculture, ANOVA, ANCOVA, Bayesian, Economics, Employment, Health, Information & Library Science, Psychology, Reliability Modeling, Research: Marketing and Opinion, Sampling Analysis, and SAS.