Resource Library

No matter how much reverence is paid to anything purporting to be statistics," the term has no meaning unless the source, relevance, and truth are all checked." is a quote by American English professor Tom B. Burnam (1913-1991). The quote is found on page 244 of his 1975 book "The Dictionary of Misinformation".

There is no free hunch. is quote by American psychologist and political scientist Robert P. Abelson (1928 - 2005). The quote is found on page 142 of his 1995 book "Statistics as a Principled Argument". It is referred to as "Abelson's Sixth Law" in a discussion of the generalizability of estimated effects.

A cartoon for use in discussing outliers. The cartoon is by New Zealand cartoonist Nick Kim (see www.lab-initio.com). This copyrighted cartoon is available for free use in classes and on course webistes at non-profit educational institutions. Commercial inquiries should be directed to the artist (e-mail:nick@lab-initio.com).

A cartoon to use in discussing the importance of indicating the variability associated with any prediction. The cartoon is the work of Theresa McCracken and appears as #5756 on McHumor.com (appearing here with a statistics-based caption change suggested by Dennis Pearl). Free for non-profit use in statistics course such as in lectures and course websites.

Lest men suspect your tale untrue, Keep probability in view. is a quote by English poet and playwright John Gay (1685 - 1732). The quote is the first two lines of the poem "The Painter who pleased Nobody and Everybody," which is fable number 18 from the from the 1727 collection "Fables" volume 1.

On this site, students can practice classifying statistics problems. They first click to check the statistical methods that they want to practice classifying. Then they click the "Submit" button to get a description of a research project that involves a statistical technique. Students then click on the technique that will most likely be used in the project. If they choose the incorrect answer, they must read the hint and try again. When they get something correct, they click on the "Next" button to try another problem.

The WISE Bootstrapping Applet can be used to demonstrate bootstrapping by creating a confidence interval for a population mean or median. The user can manipulate the population distribution, sample size, and number of resamples. An associated guide gives suggestions for teaching bootstrapping.

August 10, 2010 T&L webinar presented by Diane Fisher (University of Louisiana at Lafayette), Jennifer Kaplan (Michigan State University), and Neal Rogness (Grand Valley State University) and hosted by Jackie Miller(The Ohio State University). Our research shows that half of the students entering a statistics course use the word random colloquially to mean, "haphazard" or "out of the ordinary." Another large subset of students define random as, "selecting without prior knowledge or criteria." At the end of the semester, only 8% of students we studied gave a correct statistical definition for the word random and most students still define random as, "selecting without order or reason." In this session we will present a classroom approach to help students better understand what statisticians mean by random or randomness as well as preliminary results of the affect of this approach.

September 14, 2010 T&L webinar presented by Thomas Moore(Grinnell College) and hosted by Jackie Miller(The Ohio State University). Permutation tests and randomization tests were introduced almost a century ago, well before inexpensive, high-speed computing made them feasible to use. Fisher and Pitman showed the two-sample t-test could approximate the permutation test in a two independent groups experiment. Today many statistics educators are returning to the permutation test as a more intuitive way to teach hypothesis testing. In this presentation, I will show an interesting teaching example about primate behavior that illustrates how simple permutation tests are to use, even with a messier data set that admits of no obvious and easy-to-compute approximation.

October 12, 2010 T&L webinar presented by George Cobb(Mount Holyoke College) and hosted by Leigh Slauson (Capital University). What's the best way to introduce students of mathematics to statistics? Tradition offers two main choices: a variant of the standard "Stat 101" course, or some version of the two-semester sequence in probability and mathematical statistics. I hope to convince participants to think seriously about a third option: the theory and applications of linear models as a first statistics course for sophomore math majors. Rather than subject you to a half-hour polemic, however, I plan to talk concretely about multiple regression models and methodological challenges that arise in connection with AAUP data relating faculty salaries to the percentage of women faculty, and to present also a short geometric proof of the Gauss-Markov Theorem.