Mathematical modeling constitutes a central pillar of modern science, providing indispensable tools for analyzing, predicting and optimizing complex real-world systems across a wide array of disciplines. However, models are inherently imperfect representations of reality and various sources of error can critically impair their validity and usefulness. This paper presents an examination of error sources in mathematical modeling, distinguishing between systematic and unsystematic errors. Each type of error is discussed and illustrated with practical examples. The goal is to offer an understanding of how modeling errors arise and how they impact results.
