Safely Using Real-World Data for Teaching Statistics: A Comparison of Student Performance and Perceived Realism between Dataset Types

Academics strive to bring real-world experiences and examples into the classroom thereby creating a richer experience for the instructor and student. This goal of relevance is particularly challenging in the instruction of statistics where the instructor often must choose between “canned” simulated datasets that lack richness and relevance versus using their own research data. Real-world research datasets offer familiarity, storytelling opportunities, and an intimate understanding of the dataset providing a fuller understanding for the student; however, the public release of research data could be problematic. This article examines a solution that offers the richness and relevance of real-world datasets while safeguarding the integrity of the data and research. Experimental results support the use of these derived datasets.

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