Amortized analysis provides a more realistic view on algorithms compared to a worst-case analysis. Instead of looking at a single worst-case input, amortized analysis investigates how a sequence of inputs (e.g. repeated vector insertion) influences the running time or memory usage. For instance, the common implementation of vector insertion has an amortized insertion time complexity of since the vector doubles its capacity when full and doens’t expand when size is under capacity.