INTERNATIONAL SYSTEM OF UNITS(SI)

See ＂The International System of Units （SI）,＂ NIST Special Publication 330, B.N. Taylor, ed. （USGPO, Washington, DC, 1991）; and ＂Guide for the Use of the International System of Units （SI）,＂ NIST Special Publication 811, 1995 edition, B.N. Taylor （USGPO, Washington, DC, 1995）.

Unleashing the Power of Information Theory: Enhancing Accuracy in Modeling Physical Phenomena

When building a model of a physical phenomenon or process, scientists face an inevitable compromise between the simplicity of the model (qualitative-quantitative set of variables) and its accuracy. For hundreds of years, the visual simplicity of a law testified to the genius and depth of the physical thinking of the scientist who proposed it. Currently, the desire for a deeper physical understanding of the surrounding world and newly discovered physical phenomena motivates researchers to increase the number of variables considered in a model. This direction leads to an increased probability of choosing an inaccurate or even erroneous model. This study describes a method for estimating the limit of measurement accuracy, taking into account the stage of model building in terms of storage, transmission, processing and use of information by the observer. This limit, due to the finite amount of information stored in the model, allows you to select the optimal number of variables for the best reproduction of the observed object and calculate the exact values of the threshold discrepancy between the model and the phenomenon under study in measurement theory. We consider two examples: measurement of the speed of sound and measurement of physical constants.

Advancing Scientific Rigor: Towards a Universal Informational Criterion for Assessing Model-Phenomenon Mismatch

The escalating costs of research and development, coupled with the influx of researchers, have led to a surge in published articles across scientific disciplines. However, concerns have arisen regarding the accuracy, validity, and reproducibility of reported findings. Issues such as replication problems, fraudulent practices, and a lack of expertise in measurement theory and uncertainty analysis have raised doubts about the reliability and credibility of scientific research. Rigorous assessment practices in certain fields highlight the importance of identifying potential errors and understanding the relationship between technical parameters and research outcomes. To address these concerns, a universally applicable criterion called comparative certainty is urgently needed. This criterion, grounded in an analysis of the modeling process and information transmission, accumulation, and transformation in both theoretical and applied research, aims to evaluate the acceptable deviation between a model and the observed phenomenon. It provides a theoretically grounded framework applicable to all scientific disciplines adhering to the International System of Units (SI). Objective evaluations based on this criterion can enhance the reproducibility and reliability of scientific investigations, instilling greater confidence in published findings. Establishing this criterion would be a significant stride towards ensuring the robustness and credibility of scientific research across disciplines.