Guide to Teaching with Data in Statistics and Data Science Classrooms

Abstract

Statistics and data science educators have been concerned about data use or lack thereof in the classroom for decades. For many educators, the question is not whether to use data or not. They often want to know whether they should give clean or messy data, and whether they should spend the time to find a real dataset or simulate it. If they decide to find real data, then other questions follow about where to find it and how to provide it to students. If they provide data, then the question remains whether students will find the data engaging or not or if it will have the properties necessary to demonstrate class concepts. We provide empirically and theoretically supported recommendations on data use. Classroom data are described in different ways (e.g., real, raw, made-up, artificial, fictitious, synthetic, simulated, toy, and messy). We provide definitions of different types, sources, formats, and shapes of data and provide distinctions between classifications. We share resources for educators in bringing data to their classrooms.

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BibTeX RIS

BibTeX

@misc{dogucu2026guide,
  title = {{Guide to Teaching with Data in Statistics and Data Science Classrooms}},
  author = {Mine Doğucu and Catalina Medina and Crystal Lewis},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.00470},
  eprint = {2611.00470},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.00470},
  note = {CC BY-NC-ND 4.0},
}

RIS

TY  - UNPB
TI  - Guide to Teaching with Data in Statistics and Data Science Classrooms
AU  - Mine Doğucu
AU  - Catalina Medina
AU  - Crystal Lewis
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.00470
UR  - https://arxiv.org/abs/2611.00470
AB  - Statistics and data science educators have been concerned about data use or lack thereof in the classroom for decades. For many educators, the question is not whether to use data or not. They often want to know whether they should give clean or messy data, and whether they should spend the time to find a real dataset or simulate it. If they decide to find real data, then other questions follow about where to find it and how to provide it to students. If they provide data, then the question remains whether students will find the data engaging or not or if it will have the properties necessary to demonstrate class concepts. We provide empirically and theoretically supported recommendations on data use. Classroom data are described in different ways (e.g., real, raw, made-up, artificial, fictitious, synthetic, simulated, toy, and messy). We provide definitions of different types, sources, formats, and shapes of data and provide distinctions between classifications. We share resources for educators in bringing data to their classrooms.
ER  -

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