Resource Library

The number of degrees of freedom is usually self-evident - except for the analysis of data that have not appeared in a textbook. A quote from M.I.T. professor of management David Durand (1912- 1996) Published in a letter to the editor of "The American Statistician" June, 1970 as part of a tongue-in-cheek "Dictionary for Statismagicians." The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

A cartoon to teach about the margin of error for sample surveys. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

This lesson introduces confidence intervals and how to calculate them. A multiple choice test is given at the end.

This tutorial on the Kruskal-Wallis test includes its definition, assumptions, characteristics, and hypotheses as well as procedures for graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.

This page discusses the differences in parametric and nonparametric tests and when to use then.

This page discusses the proper procedures for multiple comparison tests and reasons behind them.

This collection of tutorials covers many statistical applications such as Pearson's Correlation Coefficient, Simple Linear Regression, One and Two Sample t-tests, Paired t-test, One-way Analysis of Variance (ANOVA), Mann-Whitney Test, Kruskal-Wallis Test, Friedman's Test, Interpreting p-values, Comparing two groups, Parametric and Nonparametric analyses, and Multiple Comparisons. The tutorials refer to the WINKS statistical software program, but they are useful for those who do not have access to WINKS.

Using cooperative learning methods, this activity provides students with 24 histograms representing distributions with differing shapes and characteristics. By sorting the histograms into piles that seem to go together, and by describing those piles, students develop awareness of the different versions of particular shapes (e.g., different types of skewed distributions, or different types of normal distributions), and that not all histograms are easy to classify. Students also learn that there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.).

Using cooperative learning methods, this lesson introduces distributions for univariate data, emphasizing how distributions help us visualize central tendencies and variability. Students collect real data on head circumference and hand span, then describe the distributions in terms of shape, center, and spread. The lesson moves from informal to more technically appropriate descriptions of distributions.

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.