04/30/2025: 1:15 PM - 2:45 PM MDT
Refereed
Room: Seminar Theater
Chair
David Han, University of Texas at San Antonio
Target Audience
Beginner
Mid-Level
Tracks
Education
Symposium on Data Science and Statistics (SDSS) 2025
Presentations
This talk will discuss the importance of brining data and data literacy to the K-12 classroom. As data is a central component in all disciplines and facets of our world, it is imperative that students graduating from high school be prepared to engage with data in their daily lives. We will present results from a series of commissioned reports by the American Statistical Association and show examples of how these recommendations can be carried out in classrooms by teachers and students.
Presenting Author
Anna Bargagliotti, Loyola Marymount University
First Author
Anna Bargagliotti, Loyola Marymount University
CoAuthor(s)
Christine Franklin, ASA
Kaycie Maddox, American Statistical Association
Robert Gould, UCLA
Susa Peters, ASA
Randall Groth
Modern statistics education increasingly needs to address the importance of scientific and statistical ethics. In our capstone course for the applied statistics minor, we spend three weeks exploring ethical issues in statistical research. This discussion begins by exploring the history of statistics and its ties to Eugenics, followed by a discussion of the reproducibility crisis. Students are encouraged to brainstorm possible causes of this crisis, and to explore the systemic causes of research fraud. We examine the concept of p-hacking and explore the replicability of p-values with a simulation exercise in class. Students develop their own sense about the reliability of p-values and learn about the concept of pre-registration, reading other pre-registrations and developing their own. To end the unit, students work in small groups to examine specific cases including Amy Cuddy, Brian Wansink, and Diedrich Stapel, and the class discusses the scale, significance, and consequences of their potential fraud. While most courses don't have the luxury of this much time, this talk will provide a number of resources including readings, videos, and podcasts that can be used in class or shared with students. In addition, it will share in-class activities, homework assignments, and possible group projects that can be used, modified, or adjusted depending on an instructor's goals and the time available. We argue that this material needs to be introduced into statistics and scientific education for all students so that students can recognize and prevent scientific misconduct and fraud.
Presenting Author
Caitlin Cunningham, Le Moyne College
First Author
Caitlin Cunningham, Le Moyne College
Introductory statistics courses often present a fragmented sequence of topics-data visualization, t-tests, ANOVA, regression-that can leave students with knowledge that is neither coherent nor transferable. This traditional approach often contrasts sharply with how most statisticians engage with data: through statistical modeling. Modeling frameworks unify diverse concepts and tools, yet introductory courses rarely place modeling at the center of instruction. This disconnect limits students' ability to apply statistical concepts flexibly, especially to advanced techniques like multivariate and computational models.
The Practicing Connections Framework, grounded in research from the learning sciences, provides a path forward. This framework posits that coherent and transferable knowledge is built when students actively practice connecting core concepts (such as the statistical model) with: real-world contexts and authentic practices, key representations (e.g., R code, graphs, General Linear Model notation), and other concepts and procedures.
The CourseKata interactive textbook, designed for introductory statistics courses, embodies this framework. Using the core concept DATA = MODEL + ERROR, CourseKata introduces the mean of an outcome variable (i.e., Y) as the simplest statistical model, builds an understanding of residuals and standard deviation as measures of error. As the course progresses, students encounter more complex models, where the value of another variable (X) is used to predict Y. This extension introduces students to group models (i.e., t-tests, ANOVA) and regression models, all within the same modeling framework. Each new variant extends the modeling core concept.
Evidence shows this method enhances transfer to advanced models, promotes equity, and shifts attitudes toward R programming positively. By centering statistical modeling in introductory courses, the Practicing Connections Framework aligns teaching with modern practice.
Presenting Author
Ji Son, Cal State LA
First Author
Ji Son, Cal State LA
CoAuthor
James Stigler, UCLA