SearcharxivSearch

arXiv · 2309.07180

How do ASA Ethical Guidelines Support U.S. Guidelines for Official Statistics?

Abstract

In 2022, the American Statistical Association revised its Ethical Guidelines for Statistical Practice. Originally issued in 1982, these Guidelines describe responsibilities of the 'ethical statistical practitioner' to their profession, to their research subjects, as well as to their community of practice. These guidelines are intended as a framework to assist decision-making by statisticians working across academic, research, and government environments. For the first time, the 2022 Guidelines describe the ethical obligations of organizations and institutions that use statistical practice. This paper examines alignment between the ASA Ethical Guidelines and other long-established normative guidelines for US official statistics: the OMB Statistical Policy Directives 1, 2, and 2a NASEM Principles and Practices, and the OMB Data Ethics Tenets. Our analyses ask how the recently updated ASA Ethical Guidelines can support these guidelines for federal statistics and data science. The analysis uses a form of qualitative content analysis, the alignment model, to identify patterns of alignment, and potential for tensions, within and across guidelines. The paper concludes with recommendations to policy makers when using ethical guidance to establish parameters for policy change and the administrative and technical controls that necessarily follow.

Explore related subjects

Keep this discovery

BibTeXRIS

Jennifer Park, Rochelle E. Tractenberg. 2023-09-12. How do ASA Ethical Guidelines Support U.S. Guidelines for Official Statistics?. https://arxiv.org/abs/2309.07180

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

KEEP EXPLORING

Related papers

The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students

Motivated by national calls for computationally enriched, data-centric instruction across the statistics curriculum, this study investigates the design, implementation, and impact of a Virtual Statistical Computing Lab (VSCL) integrated into an introductory statistics course at a medium-sized minority-serving university in the USA. The redesigned course embedded R-based coding through two virtual lab formats: Design I (a static Posit Cloud environment) and Design II (an interactive learnr-based interface). Using a quasi-experimental design across three instructional formats, traditional (no lab), Design I, and Design II, we evaluated students' conceptual learning gains, levels of data science (DS) readiness, and DS aspirations. The results indicated significant learning gains across all groups, with the highest gains observed in Design II. Students in both VSCL formats achieved greater gains in DS readiness than the traditional group, with Design II again yielding the largest gains across the demographic subgroups. Conversely, DS aspirations remained low or declined, suggesting a gap between skill acquisition and long-term interest. These findings highlight the value of structured, interactive computing environments in supporting statistical reasoning and building confidence in modern data tools. They also point to the need for intentional curricular bridges and career mentoring to help students translate early computing exposure into sustained academic and professional pathways in statistics and data science.

stat.OT

Statistical Theory in the Age of Machine-Assisted Mathematics: Rethinking How Theory Is Made and Taught

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. However, these technologies 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 they can be analyzed using a machine proof-checking. Our goal is not to propose a definitive workflow, but to stimulate reflection on how these technologies may transform theoretical research and advanced statistical education. We focus on two main aspects. First, statistical theory often compresses substantial mathematical content into expressions such as "under the usual regularity conditions". Formalization in a machine-verifiable language forces each assumption to be explicit, reveal hidden dependencies, and provide a deeper understanding of the formalized objects. Second, we argue that the statistical community could benefit from a collaborative effort to build repositories of formalized axioms, definitions, and theorems, supporting more precise and reliable theoretical developments. Finally, we discuss the role of these tools in graduate education. Just as high-level programming languages revolutionized empirical research by enabling rapid experimentation and prototyping, machine-assisted formalization may introduce a new paradigm for the development, verification, and communication of statistical theory.

stat.OT

Statistical Leadership of What? Statistics After AI

Statisticians have spent over a century arguing that we are more than calculators, usually by pointing to what else we know. AI is making that defense harder, since the list of what only statisticians can do grows shorter with each model release. AI makes claims cheap to generate and may eventually make the statistics behind them cheap too. However, a model cannot be answerable in the way that statistical practice requires. Statistical leadership then becomes a question of which claims we are there to answer for, including the ones we answer for in advance by building judgment into systems.

stat.OT