Structural disorders in various fields, from materials science across economic universe to biological systems, present complex challenges in data analysis due to their inherent variability, various uncertainties, and changing patterns. Identification of structural breaks in data helps us to tie in specific health, legal, economic, or natural changes to the time epochs in which they occurred and the types of environments and industries that are affected. Testing and estimation of unknown changes hence reveals data segmentation, regime switching, and disorder recognition. Resampling methods, such as subsampling, bootstrapping and permutation techniques, offer computationally feasible as well as theoretically efficient strategies for assessing structural irregularities.