Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.00470

Guide to Teaching with Data in Statistics and Data Science Classrooms

Mine Doğucu·Catalina Medina·Crystal Lewis
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

stat.OT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mine Doğucu, Catalina Medina, Crystal Lewis. 2026-10-30. Guide to Teaching with Data in Statistics and Data Science Classrooms. https://arxiv.org/abs/2611.00470

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Statistical Theory in the Age of Machine-Assisted Mathematics

The computational revolution is advancing at an unprecedented pace. The combination of proof-assistant technologies and generative AI tools has recently enabled the solution of complex problems in pure mathematics at a scale that seemed unattainable only a few years ago, but they have not yet become standard tools in the development of statistical theory. In this paper we do not present new theoretical results; instead, we discuss five case studies involving classical problems in statistics and describe how a machine proof checker changes the thought process behind their proofs, in order to stimulate reflection on how these technologies may transform theoretical research and advanced statistical education. Statistical theory compresses mathematical content into expressions such as ``under the usual regularity conditions''; once AI writes the proofs and machines check them, the human reader is left with the statement and its hypotheses, which is exactly what such expressions compress. Against the fear that automation erodes skills, our experience was the opposite: what changes is the job of the theorist, which moves from the steps of a proof to its statement. The new paradigm can have positive effect also on methodologists and applied statisticians, because these tools allow for quick and precise check of formal guarantees necessary to justify the application of a specific model and/or methodology. We draw consequences for refereeing, doctoral teaching, and we encourage a collective effort toward shared formal libraries for statistics. Just as high-level programming languages revolutionized empirical research, machine-assisted formalization may introduce a new paradigm for the development, verification, and communication of statistical theory.

stat.OT↗
arXiv2026

Tackling the 6/49 Lottery and Debunking Common Myths with Probabilistic Methods and Combinatorial Designs

At the end, the house always wins! This simple truth holds for all public games of chance. Nevertheless, since lotteries have existed, people have tried everything to give luck a helping hand. This article compares objective scientific approaches to tackle the 6/49 lottery: probabilistic methods and combinatorial designs. The mathematical models developed herein can be modified and applied to other lotteries. The newly constructed (49, 6, 5) covering design is introduced, which meets the Schönheim bound. For lottery designs and for covering designs, a benchmark based on probabilistic methods is presented. It is demonstrated that common attempts to outwit the odds correspond to limitations of numbers to subsets, which disproportionately reduce the chances of winning.

stat.OT↗
arXiv2026

What's Old Is New: Improved Learning from Small-Group Tutorials

The ubiquity of Large Language Models poses a challenge to student learning in homework-based college courses, where access to easy homework answers devalues the intended productive struggle. To address this concern, we propose Oxbridge-style weekly tutorials, where students present homework problems without notes in a small group with an instructor or TA. We conducted a randomized experiment in an introductory probability class to evaluate the effectiveness of tutorials, finding a significant positive effect of tutorial enrollment on exam grades. We discuss the implementation, challenges, and opportunities in this style of course design.

stat.OT↗