The Time Hierarchy Theorem states, in theory, that by providing more time, there exist strictly more problems that can be solved. However, most of these separations are very abstract and cannot be seen in a real computing environment. This paper investigates how algorithmic classes of different time complexities act under real-world time constraints. It does this by empirically evaluating standard algorithms with time complexities from O(n log n) to O(2ⁿ) and measuring the input sizes at which effective divergence is observed. The paper finds effective and observable diversions in line with theoretical expectations, empirically supporting theoretical hierarchies.