uncertainty

Accurate measurements of physical processes are essential for scientific research and technological advancement. From subatomic particles to celestial bodies, precise measurements underpin our understanding of the universe and drive innovation. However, selecting the right mathematical model to interpret experimental data can be challenging due to the complexities of real-world phenomena. Traditional model selection methods often rely on researcher intuition and experience, leading to subjectivity and potential biases. In this article, we explore the Finite Information Quantity (FIQ) approach—a novel solution that addresses these limitations.