Resource Library

Video that explains what p-values and significance levels are in hypothesis testing.

Resource that covers specific topics within significance testing, including significance levels. P-values and how to determine what qualifies as being statistically significant covered. Examples are given throughout the text to further explain the concepts.

Resource that gives a clear description of what the p-values and significance levels mean, and what statistical significance means. Graphs are used to illustrate the topics covered in this source.

A cartoon for use in teaching about the Normal distribution. The cartoon was drawn by Australian epidemiologist Matthew Freeman of the Public Health Information Development Unit at the University of Adelaide. It is free for use on course websites or in the classroom provided author acknowledgement is given (e.g. leave copyright statement on the image). Commercial uses should contact the copyright holder. The cartoon was also published under the title "A visual comparison of normal and paranormal distributions" in the Journal of Epidemiology and Community Health (2006) 60(1):6

A cartoon that can be used in teaching about summary statistics. 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.

Old statisticians never die they just become nonsignificant. Quote by Gary Ramseyer (1934 - 2012) listed as joke #56 on http://www.ilstu.edu/~gcramsey/Gallery.html Gary C. Ramseyer's First Internet Gallery of Statistics Jokes

This survey assesses statistical literacy. The survey focuses on the general use of informal statistics in everyday situations: reading and interpreting tables and graphs involving rates and percentages.

This is an example of "growing" a decision tree to analyze two possible outcomes. The tree's branches examine the two possible conditions of employee drug use with corresponding probabilities. This example looks at the final outcome probabilities of being correctly and incorrectly identified versus testing accuracy.

The assignment begins with creating a summary of and tables for the data, then walks the student through the steps of creating a hypothesis testing report. It uses the data set ncbirth200.xls, which is a random sample of 200 births from the data set ncbirth1450.xls.

This collection of free, interactive Java applets provides a graphical interface for studying the power of the most commonly encountered experimental designs. Intended to be useful in planning statistical studies, these applets cover confidence intervals for means or proportions, one and two sample hypothesis tests for means or proportions, linear regression, balanced ANOVA designs, and tests of multiple correlation, Chi-square, and Poisson. Each applet opens in its own window with sliders, which are convertible to number-entry fields, for manipulating associated parameters. Controlling for the other parameters, users can change sample size, standard deviation, type I error (alpha) and effect size one at a time to see how each affects power. Conversely, users can manipulate the power for the test to determine the necessary sample size or margin of error. Additional features include a graph option by which the program plots a dependent variable (i.e. power) over a range of parameter values; the graph is automatically updated as the parameters are changed. Each dialog window also offers a Help menu which provides instructions for using the applet. The applets can be used over the Internet or downloaded onto the user's own computer.