Year: 2026
Type: preprint
Abstract: Statistical software makes methods widely available. Good statistical work, however, requires understanding the question, how the data were collected, and what conclusions the evidence can support. As AI agents make data analysis accessible to people without statistical training, this reasoning should be built into the systems themselves. Our review of selected AI agents for data analysis and study design points to a gap in documented support for scientific inquiry with users at runtime. In this paper, we advocate designing domain-specific interactive harnesses in which statistical principles guide proactive inquiry: agents recognize missing information that could change an analysis, ask questions users may not know to raise, and use the answers to guide or revise analytical decisions. Two practical entry points illustrate how statisticians can begin building: an implemented causal-consulting skill within an existing agent environment and Model Forge, a custom harness for interactive model and algorithm development. These design examples connect statistical principles to choices about context, memory, tools, and interfaces, and motivate evaluation of both computational correctness and the scientific consequences of interaction. We conclude with a broader interdisciplinary research agenda on adaptive human-agent interaction, including selective information acquisition, evolving user preferences, and evaluation under uncertainty.
Source: arXiv (Cornell University)
Author: Zhu, Ruoqing
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223693200