What is Big-O notation used for in algorithm analysis?

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Big-O notation describes how the resources an algorithm uses—typically running time and memory—grow as the input size increases. It focuses on an upper bound, capturing the worst-case or general growth rate for large inputs, up to constant factors and ignoring lower-order terms. This lets you compare how scalable different algorithms are as n becomes large. It isn’t about the exact runtime for a specific input, and it doesn’t specify a lower bound (that would be Omega notation) or the average-case performance (which depends on input distribution). So the best description is that Big-O characterizes the upper bound on growth of runtime or space with input size to compare scalability.