Resource Library

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

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.

This tutorial explains the theory and use of Student's t-test for matched pairs and demonstrates it with an example on project quality. Data is given as well as SPSS and Minitab code.

This tutorial describes various measures of central tendency, their theory and use, and demonstrates them with an example on final exam scores. Data is given as well as SPSS and Minitab code. Key Words: Mean; Median; Mode; Variance; Standard Deviation.

This tutorial explains the theory and use of the Mann-Whitney test and demonstrates it with an example on traditional lecture versus computer-based teaching. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of the Kruskal-Wallis test and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of the Friedman Two Way ANOVA and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of the Spearman's Rank-Difference Correlation Coefficient and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.