What type of analysis is used to determine the best, average, and worst-case scenarios of an algorithm?

Time complexity analysis is the correct approach for evaluating the best, average, and worst-case scenarios of an algorithm. This type of analysis focuses on how the runtime of an algorithm changes with respect to the size of the input data.

In time complexity analysis, specific scenarios are considered to determine how an algorithm performs under various conditions. The best-case scenario represents the minimum time required for the algorithm to complete, typically when the input is in a favorable arrangement. The average-case scenario reflects the expected time for typical inputs, while the worst-case scenario considers the maximum time that the algorithm could potentially take, often based on the least favorable input condition.

Understanding these scenarios helps developers choose the most efficient algorithm based on the type of problem and the nature of the input data they expect, making time complexity analysis essential for algorithm evaluation and optimization.