ArXiv [Preprint]. 2026 Jun 8:arXiv:2606.10109v1.
ABSTRACT
Computational simulation provides a powerful toolkit for in silico experimentation. However, while the field has developed best practices for the design and implementation of such models, there remains ambiguity in discussions about how to understand and/or interpret their results due to their inherent ability to overwhelm traditional frequentist statistics by simply increasing the number of trials simulated. This fails the discipline in two ways: first, it leaves the community unsure of what constitutes a best practice for uniform understanding, and second, it potentially overburdens computational studies that burn clock cycles solely to ensure “enough runs to satisfy peers” without any theoretical underpinning for a definition of “enough”. We propose a simple and straightforward standard for when to stop simulating additional trials, the Ω test, designed to be analogous to the function of traditional frequentist P-tests. Community adoption of a reasonable and uniform standard will permit more efficient computational experimentation and clearly communication/interpretation of the findings discovered in this way.
PMID:42339494 | PMC:PMC13286023
