Consensus Study Report

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This study was supported by a contract and grants between the National Academy of Sciences and the National Institutes of Health (Contract No. HHSN263201800029I/75N 98024F00006), the National Science Foundation (Grant No. 2426575), and the National Security Agency (Grant No. H98230-24-1-0019). Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

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Digital Object Identifier: https://doi.org/10.17226/29292

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2026. Frontiers of Statistics in Science and Engineering: 2035 and Beyond. Washington, DC: National Academies Press. https://doi.org/10.17226/29292.

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COMMITTEE ON THE FRONTIERS OF STATISTICS IN SCIENCE AND ENGINEERING: 2035 AND BEYOND

KATHERINE B. ENSOR, Rice University, Chair

LANCE A. WALLER, Emory University, Vice Chair

RINA F. BARBER (NAS), The University of Chicago

AMY BRAVERMAN, Jet Propulsion Laboratory, California Institute of Technology

LORIN CRAWFORD, Microsoft Research

DAVID B. DUNSON, Duke University

OMAR GHATTAS, The University of Texas at Austin

FRAUKE KREUTER, University of Maryland

XIHONG LIN (NAS/NAM), Harvard University

BRIAN REICH, North Carolina State University

STEPHAN R. SAIN, Jupiter Intelligence

AARTI SINGH, Carnegie Mellon University

DANIELA M. WITTEN, University of Washington

TIAN ZHENG, Columbia University

Study Staff

BRITTANY SEGUNDO, Program Officer, Board on Mathematical Sciences and Analytics (BMSA), Study Director

MICHELLE K. SCHWALBE, Senior Program Director, Physical Sciences, Systems, and Infrastructure Program Area

ERIK SVEDBERG, Deputy Director, BMSA and National Materials and Manufacturing Board

SAMANTHA KORETSKY, Research Associate, BMSA

HEATHER LOZOWSKI, Senior Finance Business Partner

MICA PACHECO, Associate Program Officer, BMSA

JOE PALMER, Senior Project Assistant, BMSA

Reviewers

This Consensus Study Report was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published report as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process.

We thank the following individuals for their review of this report:

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations of this report nor did they see the final draft before its release. The review of this report was overseen by ALICIA CARRIQUIRY (NAM), Iowa State University, and ELIZABETH STUART (NAM), Johns Hopkins Bloomberg School of Public Health. They were responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authoring committee and the National Academies.

Preface

The field of statistics is the language of science and innovation. Throughout history, the ability to examine data and information (or lack thereof) critically and provide actionable insight and wisdom grounded in an understanding of the potential biases and uncertainties around these measurements has elevated science and engineering in service to humanity. The field is broad and advances in response to novel ideas and breakthroughs both from within the field itself and its intersections with other disciplines. Statistical advances represent both new statistical thinking and new insights, data, and analytic paradigms from other fields including mathematics and computer science, as well as through collaborations with specific disciplines within science, engineering, medicine, and beyond. This “melting pot” of innovation results in statistics evolving as a dynamic field of inquiry. Statistics also has its own deep roots that grow with new data, assumptions, and computational capabilities, all of which motivate new questions that were unthinkable or considered impossible only a few years earlier. As such, the frontiers of statistics derive from within the field itself and from input into statistics from other fields, and the frontiers provide input from statistics back to these fields.

Because of the close relationship between statistics and other fields in science and engineering, many technological advances today stem from foundational advancements in statistics over the past few decades. Statistics provides the scientific basis for artificial intelligence (AI) and machine learning, which are revolutionizing the world and will bring societal transformations in ways that are difficult to predict. Statistics affords a pathway for transparent, responsible, and trustworthy AI, which is a necessary and important requirement, as these technologies are interwoven with the fabric of society.

Frontiers of Statistics in Science and Engineering: 2035 and Beyond represents a year-long exercise to understand the breadth and depth of the field of statistics and implications for its trajectory over the next 10 years. This intense effort not only reaffirmed the critical importance of the field but also demonstrated its expanded relevance amid the technological advances we all navigate. The life cycle of statistical innovation will remain central to science and engineering progress as statisticians work collaboratively, as new questions, new data types, and new methodologies arise.

We would like to thank our sponsors (in alphabetical order): the National Institutes of Health, the National Science Foundation, and the National Security Agency for their support for this study and for outlining such important objectives. The statement of task focuses on science and engineering, which, for the purposes of this study, includes health and medicine. Our report has retained that focus. The field of statistics also has a long history of contributions to and reciprocal development from the social sciences, but this is beyond the scope of the report.

This impactful study was made possible through the dedication and significant contributions of an exceptional committee, including the National Academies of Sciences, Engineering, and Medicine staff. All committee members brought expertise across the breadth of the field. We also recognize the invaluable suggestions and insights from the independent review panel that read a previous draft of the report. The collective and tireless commitment to science, technology, and society was evident throughout the process. We are grateful for the extensive contributions of leaders in our field through various panel discussions, as well as those who provided direct input via our online form.

We want to thank our community and the National Academies for entrusting us with this important role. We hope that by 2035 the study will have paved the way for lasting, transformative impact by bringing the full statistical endeavor to bear.

Katherine B. Ensor, Chair
Lance A. Waller, Vice Chair
Committee on the Frontiers of Statistics in Science and Engineering: 2035 and Beyond
February 2026

Acknowledgments

We are grateful to the many scholars and leaders who contributed their time and expertise to the committee’s information-gathering efforts. Special thanks go to all the speakers who briefed the committee:

Sivan Aldor-Noiman (Banff Advisors), Veera Baladandayuthapani (University of Michigan), Bonnie Berger (Massachusetts Institute of Technology), Catherine Calder (The University of Texas at Austin), Beth Chance (California Polytechnic State University), Wei Chen (Northwestern University), Peter Chien (University of Wisconsin–Madison), Jan Dasgupta (Washington State University), Peng Ding (University of California, Berkeley), Hani Doss (University of Florida), John Duchi (Stanford University), Yulia Gel (Virginia Tech University), Bonnie Ghosh-Dastidar (RAND), Carlo Graziani (Argonne National Laboratory), Trevor Hefley (Kansas State University), Dave Hunter (The Pennsylvania State University), Roshan Joseph (Georgia Institute of Technology), Amarjot Kaur (Merck), Michael Kosorok (University of North Carolina), Donna LaLonde (American Statistical Association), Nicole Alana Lazar (The Pennsylvania State University), Hongzhe Li (University of Pennsylvania), Lexin Li (University of California, Berkeley), Lester Mackey (Microsoft Research), Monnie McGee (Southern Methodist University), Shari Messinger (University of Miami), Jeffrey Morris (University of Pennsylvania), Vijay Nair (Wells Fargo), Dan Nettleton (Iowa State University), Betsy Ogburn (Johns Hopkins University), Eunchun Park (University of Arkansas), Robert Perera (Virginia Commonwealth University), Annie Qu (University of California, Irvine), Ricky Rambharat (Northrop Grumman), Abel Rodriguez (University of Washington), Christopher Saunders (South Dakota State University), David Schimel (Jet Propulsion Laboratory, California Institute of Technology), Lyndsay Shand (Sandia National Laboratories),

Contents

BOXES, FIGURES, AND TABLE

ACRONYMS AND ABBREVIATIONS

SUMMARY

1 INTRODUCTION

Statistical Contributions

Statistical Thinking in Scientific Developments

Report Development and Structure

2 STATISTICS: A SNAPSHOT OF A DYNAMIC FIELD

Theoretical Contributions

Methodological Contributions

Applied Contributions

The Life Cycle of Statistical Innovation

Evolving Frontiers

Catalyzing Frontiers Across Domains

3 THE CHANGING NATURE OF DATA

Evolving Conceptual Foundations of Data

Expanding Models of Data Fusion

Opportunities and Challenges

4 STATISTICS ACROSS THE SPECTRUM

The Spectrum

Collaboration with Allied Fields

Evolution of Synergistic Collaboration

Building a Collaboration Ecosystem

5 STATISTICAL INNOVATIONS THAT ENABLE NEW SCIENTIFIC AND TECHNOLOGICAL ADVANCES

Stage 1: Discovery

Stage 2: Design

Stage 3: Decisions

Stage 4: Deployment and Sustainment

The Risk to Science and Technology Without Statistics

6 STATISTICS EDUCATION, TRAINING, AND WORKFORCE DEVELOPMENT

Statistics Education: Opportunities and Significance for 2035

Frontiers of Statistics Education: What and How

7 RECOMMENDATIONS

Future of Statistics

Statistics for Artificial Intelligence

Interfaces

Education

Public–Private Partnerships

Academic Hiring and Promotion

REFERENCES

APPENDIXES

A Statement of Task

B Call for Perspectives and Public Meeting Agendas

C Committee Member Biographical Information

Boxes, Figures, and Table

BOXES

1-1 Statement of Task

2-1 Statistical Development and the Life Cycle of a Statistical Innovation

3-1 Data and Ecology

4-1 The Impact of Statistics in Finance

5-1 Data-Driven Science and Genomics

5-2 Design of Experiments in Precision Agriculture

5-3 Google Flu Trends

FIGURES

S-1 The life cycle of statistical innovation

S-2 Statistics to enable science and technology advances

2-1 The life cycle of statistical innovation

4-1 Statistics: The core of data-driven scientific progress

5-1 Statistics to enable science and technology advances

TABLE

5-1 Stages of Scientific and Technological Advancement

Acronyms and Abbreviations