  Simulations play a crucial role in the modern scientific process. Yet despite
(or due to) their 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 Data
Science simulation should satisfy. Modularity and Efficiency support the
Computability of a study, encouraging clean and flexible implementation.
Realism and Stability address the conceptualization of the research problem:
How well does a study Predict reality, such that its conclusions generalize to
new data/contexts? Finally, Intuitiveness and Transparency 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 deconstructing a simulation through the lens of our
framework to demonstrate its practical utility. By contributing 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.
