Elliott, Corrine F. and Duncan, James P. C. and Tang, Tiffany M. and Behr, Merle and Kumbier, Karl and Yu, Bin (2026) Designing a Data Science Simulation with MERITS: A Primer. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 35 (2). pp. 704-719. ISSN 1061-8600, 1537-2715
Full text not available from this repository. (Request a copy)Abstract
Simulations play a crucial role in the modern scientific process. Yet despite (or due to) this ubiquity, the Data Science community shares neither a comprehensive definition for a "high-quality" study nor a consolidated guide to designing one. Inspired by the Predictability-Computability-Stability (PCS) framework for "veridical" Data Science, we propose six MERITS that a simulation study should satisfy. (M odularity and E fficiency support the computability of a study, encouraging clean and flexible implementation. R ealism and S tability address the conceptualization of the research problem: How well does a study predict reality, such that its conclusions generalize to new data/contexts? Finally, I ntuitiveness and T ransparency encourage good communication and trustworthiness of study design and results.) Drawing an analogy between simulation and cooking, we moreover offer (a) a conceptual framework for thinking about the anatomy of a simulation "recipe"; (b) a baker's dozen in guidelines to aid the Data Science practitioner in designing one; and (c) a case study demonstrating the practical utility of our framework by using it to autopsy a preexisting simulation study. With this "PCS primer" for high-quality Data Science simulation, we seek to distill and enrich the best practices of simulation across disciplines into a cohesive recipe for trustworthy, veridical Data Science.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CLINICAL-TRIAL SIMULATION; INFORMATION; ROBUSTNESS; PROTOCOL; PCS framework; Simulation design; Trustworthy AI; Veridical data science |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Maschinelles Lernen (Prof. Dr. Merle Behr) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 18 Jun 2026 05:39 |
| Last Modified: | 18 Jun 2026 05:39 |
| URI: | https://pred.uni-regensburg.de/id/eprint/67249 |
Actions (login required)
 |
View Item |
