Average-case analysis is a method used to evaluate the expected performance of an algorithm by considering all possible inputs and their likelihood of occurrence. This approach helps in providing a more realistic measure of an algorithm's efficiency compared to worst-case analysis, which only considers the most unfavorable inputs. Average-case analysis is particularly useful when dealing with randomized algorithms and data structures, where typical performance can differ significantly from the worst-case scenario.
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