Resource Library

Power tends to corrupt, and absolute power corrupts absolutely. is a famous quote of English historian Sir John Dalberg-Acton (1834 - 1902). Of course, Lord Acton was not referring to statistical hypothesis testing when he made the remark in an April 1887 letter to Mandell Creighton. However, the widespread knowledge of the quote by students makes it an interesting way to cover the idea that statistical significance is not the same as practical significance.

Sampling Samba is a video that may be used to discuss and compare various methods of sampling. The methods described include random sampling, systematic sampling, stratified sampling, and cluster sampling. The video was written by Camilla Guatteri (SeeYouGee on You-Tube) and edited by Alessandro Pederzoli.

A cartoon to teach the idea that the mean of a distribution is found by integrating xf(x).

Oh, people can come up with statistics to prove anything, Kent. 14% of people know that. This is a quote from the cartoon character Homer Simpson created by cartoonist Matt Groening (1954 - ) in 1987. The quote occurs in an episode of "The Simpsons" entitled "Homer the Vigilante" that originally aired on January 6, 1994. This episode was written by John Swartzwelder (1950 - )

A study in the Washington Post says that women have better verbal skills than men. I just want to say to the authors of that study: 'Duh.' This a quote from American comedian and talk show host Conan O'Brien (1963 - ) delivered on his TV show "Late Night with Conan O'Brien".

The best way to predict the future is to invent it. This is a quote by American computer scientist Alan C. Kay (1940 - ). The quote was said at a 1971 meeting of Xerox Corporation's Palo Alto Research Center.

September 22, 2009 Activity Webinar presented by Diane Evans, Rose-Hulman Institute of Technology and hosted by Leigh Slauson, Capital University. This webinar is based on an activity found at www.lhs.logan.k12.ut.us/~jsmart/tank.htm and other on-line resources (see references). During World War II, the British and U.S. statisticians used estimation methods to deduce the productivity of Germany's armament factories using serial numbers found on captured equipment, such as tanks. The tanks were numbered in a manner similar to 1, 2, 3, ..., N, and the goal of the allies was to estimate the population maximum N from their collected sample of serial numbers. The purpose of this activity is to introduce students to the concept of an unbiased estimator of a population parameter. Students develop several estimators for the parameter N and compare them by running simulations in Minitab. Extra materials available for download free of charge.

August 25, 2009 Activity webinar presented by Michelle Everson, University of Minnesota and hosted by Leigh Slauson, Capital University. In a classroom setting, students can engage in hands-on activities in order to better understand certain concepts and ideas. Replicating hands-on activities in an online environment, however, can be a challenge for instructors. The purpose of this webinar is to present an applet that was created to replicate a "Post-it Note" activity commonly used in classroom sections of an undergraduate introductory statistics course at University of Minnesota. The Post-it Note activity is meant to help students develop a more conceptual understanding of the mean and the median by moving a set of Post-it Notes along a number line. During the webinar, participants have an opportunity to see and experience just how online students are able to interact with an applet named the "Sticky Centers" applet, and the webinar presents the kinds of materials and assignments that have been created to use in conjunction with this applet. The webinar ends with a preview of a newer applet that is being developed in order to replicate the famous "Gummy Bears in Space" activity (presented in Schaeffer, Gnanadesikan, Watkins & Witmer, 1996). A supplemental student handout is available for download free of charge.

This Flash based applet simulates data from a case study of treatments for tumor growth in mice. This simulation allows the user to place mice into a control and treatment groups. The simulation then compares the difference in the groups based on this haphazard selection to those of a truly random assignment (the user may also create multiple random assignments and examine the sampling distribution of key statistics). The applet may be used to illustrate three points about random assignment in experiments: 1) how it helps to eliminate bias when compared with a haphazard assignment process, 2) how it leads to a consistent pattern of results when repeated, and 3) how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments. Key Note for Instructors: The data are drawn from a real experiment with an effective treatment but where the response is correlated with animal age and size (so tumor size will tend to be smaller in the treatment group when measured at the end of a randomized experiment but animal age and size should not be). Typically people choosing haphazardly will tend to pick larger/older animals for the treatment group and thus create a bias against the treatment.