Resource Library

A poem consisting of two quasi Haikus that can used in discussing the Cramer-Rao lower bound on the variance of a normally distributed statistic. The poem was written by Ming-Lun Ho of Chabot College and was given a third place award in the 2015 A-mu-sing contest.

A poem for teaching about the Cramer-Rao lower bound on variance of estimators. The poem was written by Ming-Lun Ho, Chabot College. It won third place in the non-song category of the 2015 A-Mu-Sing competition.

A video to teach about the central limit theorem and various issues in one-sample hypothesis testing. The lyrics and video were created by Scott Crawford from the University of Wyoming. The music is from the 1988 song "I'm Gonna Be (500 miles)" by the Scottish band The Proclaimers. The video took second place in the video category of the 2013 CAUSE A-Mu-sing competition. Free for non-profit use in classroom and course website applications.

A cartoon to teach about outliers in scatterplots. The cartoon is #114 in the "Life in Research" series at www.vadio.com. Free to use with attribution in the classroom or on course websites.

Oh, well, this would be one of those circumstances that people unfamiliar with the law of large numbers would call a coincidence. is a quote spoken by Sheldon Cooper (2007 - ) a character on the CBS comedy show "The Big Bang Theory" played by Jim Parsons (1973 - ). The quote occurred in Season 1 episode 4 that first aired in October, 2007.

This applet is designed to allow users to explore the relationship between histograms and the most typical summary statistics. The user can choose from several types of histograms (uniform, normal, symmetric, skewed, etc.), or can create their own by manipulating the bars of the histogram. The statistics available for display are mean, median, mode, range, standard deviation, and interquartile range. Also available is a "Practice Guessing" option, in which the values of the statistics are hidden until the user has entered guesses for each value.

This joke can be used in a discussion of how sample size affects the reliability of the sample mean. The joke may be found amongst the extensive Science Jokes resources at www.newyorkscienceteacher.com

A cartoon to teach the idea that averages are less variable than individual values. The cartoon is free for use on course websites or in the classroom. Commercial uses must contact the copyright holder - British cartoonist John Landers (cartoons@landers.co.uk) who drew this cartoon based on an idea from Dennis Pearl.

This in-class demonstration combines real world data collection with the use of the applet to enhance the understanding of sampling distribution. Students will work in groups to determine the average date of their 30 coins. In turn, they will report their mean to the instructor, who will record these. The instructor can then create a histogram based on their sample means and explain that they have created a sampling distribution. Afterwards, the applet can be used to demonstrate properties of the sampling distribution. The idea here is that students will remember what they physically did to create the histogram and, therefore, have a better understanding of sampling distributions.

This activity represents a very general demonstration of the effects of the Central Limit Theorem (CLT). The activity is based on the SOCR Sampling Distribution CLT Experiment. This experiment builds upon a RVLS CLT applet (http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/) by extending the applet functionality and providing the capability of sampling from any SOCR Distribution. Goals of this activity: provide intuitive notion of sampling from any process with a well-defined distribution; motivate and facilitate learning of the central limit theorem; empirically validate that sample-averages of random observations (most processes) follow approximately normal distribution; empirically demonstrate that the sample-average is special and other sample statistics (e.g., median, variance, range, etc.) generally do not have distributions that are normal; illustrate that the expectation of the sample-average equals the population mean (and the sample-average is typically a good measure of centrality for a population/process); show that the variation of the sample average rapidly decreases as the sample size increases.