The National Academies will assemble an interdisciplinary ad hoc committee to produce a forward-looking assessment of the current state of the statistical sciences and emerging opportunities for the discipline and its stakeholders as they look ahead to 2035 and beyond. The study may make recommendations to funding agencies on how to adjust and expand their portfolios of activities and partnerships to reflect the evolution of the field and strengthen the impact of the discipline.
Specifically, the study will assess:

• Statistical research. The state of research in the statistical sciences, examining aspects such as the significance of recent developments and highlighting current and emerging trends, challenges, and directions;
• Statistical research in allied fields. Interactions between the statistical sciences and the mathematical, computational, engineering, materials science, and related fields such as biostatistics and probability; the statistical foundations that underpin and motivate machine learning, artificial intelligence, the internet, and data science; the growth of data-intensive research arising in fields that incorporate statistical and data science thinking; and the role of statistics as a bridge to translational and use-inspired research;
• Statistical needs to support scientific and technological advances. Urgent needs for statistical innovation in areas such as verification, validation, and uncertainty quantification; privacy and security; and working with heterogeneous and complex data, among others, to enable responsible use of nascent scientific and technological developments like large language models; complex and dynamic systems (e.g. digital twins); hybrid models and suites of models that rely on both physics-based and data-driven approaches; broad data gathering and processing efforts; and policies around open data sharing;
• Importance of statistical sciences and strength of interdisciplinary collaborations. The role of statistical sciences in key research domains for American competitiveness, including manufacturing, materials science, finance and blockchain, biomedical and biological sciences, public health, medicine, geosciences, environmental health and science, and energy applications; and
• Statistical education, training, and workforce development. Approaches toward statistics education and training to enhance workforce development across science and engineering domains (both within statistics and in fields that incorporate statistical thinking, such as artificial intelligence and data science) for a myriad of data-rich applications relevant to industry and technology, innovation and economic competitiveness, health and well-being, national security, and other areas of national interest.