This music video describing the meaning/interpretation of an influential point in a regression analysis was created by Mary McLellan, a teacher at Aledo High School in Texas, who wrote the lyric and performed and produced the video. The song is sung to the tune of the 1978 song “You’re the one that I want” from the movie Grease. The song was part of a pair of songs that took third place in the 2019 A-mu-sing Contest.
This music video describing the problem with extrapolating beyond the range of the data in making predictions was created by Mary McLellan, a teacher at Aledo High School in Texas, who wrote the lyric and performed and produced the video. The song is sung to the tune of the 1984 Bruce Springsteen hit “Born in the U.S.A.” The song was part of a pair of songs that took third place in the 2019 A-mu-sing Contest.
The lyrics and the direction for this video were by high school student Jordyn Gross with acting by the students in Mr. Schlaegel's 2018 AP Statistics course at Burlington Township High School. The video uses the music from Taylor Swift's 2012 hit song by the same name. The video earned fifth place in the song/video category of the 2019 A-mu-sing Contest and is designed to discuss the meaning of Type I and Type II errors in hypothesis testing situations.
The script and performances in this video were by students Emelie Andersson and Seongyun (Harry) Lee from the University of Toronto and took an honorable mention in the Song/Video category of the 2019 A-mu-sing Contest. The video is designed to facilitate discussion of the strength of evidence in observational studies.
The lyrics and music for this rap were written by Parker Kain, an undergraduate student at Northern Kentucky University, that took second place in the Song/Video category of the 2019 A-mu-sing contest (Parker Kain also performed the song at the banquet of the 2019 USCOTS). The song facilitates discussion of the different components of a confidence interval (estimate, margin of error, and confidence multiplier) and interpreting the interval properly and in the context of the real world problem under study.
The lyrics and music for this video were written by Greg Crowther, from Everett Community College in Washington and the performance in the video is by Monty Harper and Friends © 2019. The video took first place in the 2019 A-mu-sing Contest. The lyrics were inspired by the blog post "Reading Clickbait | Stats Chat" and is designed to encourage students to think about whether a study makes sense and is giving believable results.
This cartoon was created by Martha Pienkowski, an undergraduate student at the University of Toronto at Mississauga, and won an honorable mention in the 2019 A-mu-sing Contest. The cartoon reviews a comparison about the assumptions and use among various hypothesis test methods. The cartoon compares the z-test, the t-test, and nonparametric alternatives like the sign test and the Wilcoxon test in paired and unpaired situations.
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 was created by Jashandeep Nijjar and Ajandan Nandakumar, undergraduate students from the University of Toronto at Mississauga, and took second place in the 2019 A-mu-sing Contest. The cartoon is designed to help in teaching about a type of bias in sample surveys. It depicts a situation when a satisfaction survey about a restaurant is given out on the grand opening night when they are giving out free food and thus, spuriously gives highly positive results about the restaurant's quality.
This cartoon was created by Austin Boyd from University of Tennessee and took first place in the cartoon category of the 2019 A-mu-sing Contest. The cartoon provides a humorous way to facilitate conversation about the multiple comparisons caveat (that the chance of getting at least one significant result grows with the number of things being tested) and the large sample caveat (that it is more likely to see small p-values with smaller effect sizes when you have a larger sample size).