Executive Summary

American Statistical Association logo over an illustration of distribution curves and scattered data points

For more than three decades, the statistics education community has recognized that students develop conceptual understanding by working with real data that matter to them. George Cobb’s call for “more data, less lecturing” shaped the 2005 GAISE College Report and carried through the 2016 revision. In 2026, it is easier than ever to act on this commitment. Data about students’ own lives and communities are abundant, tools for exploring them are widely available, and evidence-based, data-informed decision making is essential for our world. The report extends its guidance to introductory data science and considers how artificial intelligence technologies are changing the education landscape.

This is a living report. It is now a website rather than a static PDF document and will be updated as the field and its tools continue to change. Substantive changes will be documented in a change log.

The Ten Recommendations

The report is organized around ten recommendations for introductory statistics and introductory data science courses. They fall into four themes.

Doing statistics and data science

Students should experience the full process of working with data: formulating questions, examining and analyzing data, and communicating results clearly, orally and in writing, with honest attention to what the data can and cannot support. Responsible and ethical conduct is woven through every stage of that process.

  • Recommendation 1: Teach statistics and data science as processes of gleaning insights from data to inform evidence-based decisions.
  • Recommendation 2: Emphasize effective written and oral communication of results from data, with attention to the scope and limitations of conclusions.
  • Recommendation 7: Emphasize responsible and ethical conduct in the collection and use of data and in their analysis.

Choosing what to teach

Conceptual understanding takes priority over hand calculation and formula manipulation. Courses should use real data with context and purpose, and should build multivariable thinking into the course from the start rather than treating it as an add-on.

  • Recommendation 3: Focus on conceptual understanding rather than algebraic manipulation and formulas.
  • Recommendation 4: Integrate real data with a context and purpose throughout the course. Select data that are meaningful and engaging to the students.
  • Recommendation 5: Encourage multivariable thinking.

Using modern tools

Software and apps should be central to how students explore concepts and work with data, reducing the algebraic burden of analysis so that students can focus on interpretation. This includes the appropriate, vetted use of AI.

  • Recommendation 6: Incorporate software/apps to explore concepts and work with data.

Supporting student learning

Evidence-based, active pedagogies; a mix of formative and summative assessment; and deliberately inclusive practices help all students succeed.

  • Recommendation 8: Employ evidence-based pedagogies that actively engage students in the learning process.
  • Recommendation 9: Use a variety of formative and summative assessments to improve teaching and learning.
  • Recommendation 10: Implement inclusive strategies that create learning environments where all students are valued.

Explore the Recommendations

What Is New in This Edition

The 2016 report offered six recommendations: teach statistical thinking, focus on conceptual understanding, integrate real data with a context and purpose, foster active learning, use technology to explore concepts and analyze data, and use assessments to improve and evaluate student learning. Those commitments remain at the core of this edition. What is new:

  • Broader scope. The guidance now covers introductory data science as well as introductory statistics.
  • Expanded recommendations. Communication, ethics, multivariable thinking, and inclusion each receive their own recommendation, and the guidance on assessment and active learning has been strengthened and grounded in current research.
  • Attention to AI. The report considers how AI affects what students learn, how they work with data, and how instructors teach and assess.
  • Student learning outcomes. A set of course-level outcomes for introductory statistics complements the instructor-facing recommendations by describing what students should know and be able to do.
  • A living report. The report is now a website rather than a static PDF document and will be updated as the field and its tools continue to change, with substantive changes documented in a change log.

How to Use This Report

Each recommendation links to supporting discussion, resources, and examples. Instructors can begin with the recommendations, then turn to the student learning outcomes for introductory statistics or the landscape information about introductory data science to shape course design and assessment, adapting both to their own students, courses, and institutions. Department chairs, deans, and curriculum committees can use the recommendations and outcomes as a shared framework for program review and course development.

How to Cite This Report

Because this is a living report, the citation should include the date the website was viewed:

American Statistical Association. (2026). Guidelines for Assessment and Instruction in Statistics and Data Science Education (GAISE) College Report. Retrieved Month DD, YYYY, from https://asacollegegaise.org