This cartoon caption can be used to illustrate the difference between continuous and discrete distributions. The cartoon was used in the February 2025 CAUSE cartoon caption contest and the winning caption was written by retired AP Statistics teacher Jodene Kissler. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
This cartoon was created Jona Gjevori and Ahmed Salam, when they were undergraduate students at the University of Toronto at Mississauga. The cartoon won an honorable mention in the 2019 A-mu-sing Contest and is designed to humorously facilitate the discussion of issues of generalizing to the population of interest (e.g. in generalizing results in animal students to assume validity for humans without further testing).
This cartoon is a meme created by Amy Finnegan from Duke University that received an honorable mention in the 2019 A-mu-sing Contest. The meme can be used to facilitate class discussions of the difference between an estimate being precise versus being accurate. The dog represents an estimate and the dog bed represents the target (parameter). When the dog is curled up that would indicate high precision and when the dog is spread out that would represent low precision. When the dog is in the bed that would indicate accuracy and when the dog is not in the bed, that would indicate lack of accuracy. (Note: in classes where the language of “reliability” is used instead of “precision,” the meme can be renamed Accuracy vs Reliability and the representations in discussions should then be changed accordingly.)
This poem, with an accompanying video reading of the poem by Michael A. Posner from Villanova University, took first place in the poetry category of the 2025 A-mu-sing Contest. The poem is designed to teach about word (or term) frequencies in text mining which involves thoughtful construction in defining the actual measurements to use. Instructors might have students go over this poem and then discuss how to define what words or stems of words should be included or excluded in a different textual application.
A joke to teach the idea that the average of independent measurements are more reliable than individual measurements from the same process. The joke should help start a discussion of the importance of the independence assumption in this idea. The joke was written by Dennis Pearl, Penn State University and Larry Lesser, The University of Texas at El Paso in September, 2022.
An interesting sestina poem to discuss measurement scales and can also be used while discussing spurious correlations if the teacher provides a guiding question such as “What part of the poem describes the relationship between quantitative variables, rather than just descriptions of quantitative variables? Are those relationships examples of 'Spurious Correlations' (per the title of the poem)? Explain briefly." If the students need further help, the instructor might suggest that they focus on the second to last stanza. The was written by Jules Nyquist, the founder of Jules' Poetry Playhouse, a place for poetry and play and published in the Journal of Humanistic Mathematics (2022) v. 12 #2 p.554.
Explore the Hubble Deep Fields from a statistical point of view. Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.
This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The emphasis in this course will be understanding statistical testing and estimation in the context of "omics" data so that you can appropriately design and analyze a high-throughput study. Since the measurement technologies are evolving rapidly, important objectives of the course are for students to gain a basic understanding of statistical principles and familiarity with flexible software tools so that you can continue to assess and use new statistical methodology as it is developed for new types of data.
By the end of the course, you should be able to tailor the analysis of your data to your needs while maintaining statistical validity. You should come out of the course with insight so that you can assess the validity of new statistical methodologies as they are introduced as well as understand appropriate statistical analyses for data types not discussed in the class.
Perfect for students and teachers wanting to learn/acquire materials for this topic.