DIGITAL LIBRARY
DESIGNING AN INTRODUCTORY STATISTICS COURSE THAT EXCITES: “INSIDE THE NUMBERS”
University of San Francisco (UNITED STATES)
About this paper:
Appears in: ICERI2013 Proceedings
Publication year: 2013
Pages: 5363-5371
ISBN: 978-84-616-3847-5
ISSN: 2340-1095
Conference name: 6th International Conference of Education, Research and Innovation
Dates: 18-20 November, 2013
Location: Seville, Spain
Abstract:
No undergraduate course has accumulated a more dismal reputation over the years than introductory statistics. It makes little difference if you are a business, education, economics, psychology, or sociology student, the story is the same. Students typically dread taking statistics. How can this be? Why does the mere mention of the name of statistics cause such a radioactive response?

For most teachers of statistics, unlike Sophocles, “killing the messenger” might indeed be warranted: the primary blame of the unpopularity of statistics is often due to an unimaginative teaching pedagogy along with dreadfully boring data sources used. “Inside the Numbers: Understanding Inferential Statistics” is an undergraduate statistics elective that is a dramatic exception: it typically is filled to capacity within the first few hours of preregistration—10 weeks before each term it is offered! Here are a few reasons why.

1. The course uses data from public, real world internet sports sources including ESPN, EuroSports, SkySports, FOX Sports, Yahoo Sports, and STATS Inc. Students work in small teams that must find and download all data needed to address the assignments made for each class session workshop.
2. Inside the Numbers also uses a non-traditional teaching pedagogy that makes it possible to introduce and illustrate, for example, the Central Limit Theorem and confidence intervals—including the crucial conceptual interplay between sample size, differences between groups, and data variability—using full class participation during the first two 110-minute sessions of the course.
3. Class members are told to forget the fact that they are “in class” but to instead assume the roll of one of the support analysts working a network sports show that must find, organize, and assess the value of specific sports data collections essential to be used later during an on-the-air broadcast.

This gives the students a sense of urgency and importance. A few assignment examples include:
• A 2012 Forbes Magazine article identifies average sports salaries for major sports. Find independent sources that provide individual player salaries for each team-sport combination. Compare your sample statistics with the mean values in the Forbes article and determine if the differences found are accidental or real. Be sure to include your specific Internet URLs for all data sources used.
• The WTA has collected a sample of Serena Williams’ service speed (first serve) generated during the 2013 Internazionali BNL d'Italia Tournament. Compare these sample findings with the mean first serve average of 109.8 miles per hour stated by the ATA for the top 100 men. What is the chance the difference between Ms. Williams’ service speed and the 109.8 mph value is random? Explain your findings in simple terms so that both the broadcaster and audience will be able to appreciate your analysis.
• Does the amount of money a major league baseball team spends on player salaries influence how well its franchise performs as measured by its league winning percentage? Find MLB team financial expenditures for player salaries for the past season from one of our internet sports data sources and determine if the relationship is believable or not. What do you conclude? Again, explain in highly visualisable terms. How can teams use these findings, if at all? Again, reference all Internet URLs for the data you have selected for analysis.
Keywords:
Innovative statistics pedagogy, sports analysis, non-traditional student responsibilities.