Best, Worst, and Average Case

So far, we have mostly discussed worst-case complexity, the maximum possible work for the most difficult input. But algorithms can behave very differently depending on the data they receive. A sorting algorithm might fly through data that is already nearly sorted but struggle with random data. Understanding best, worst, and average case analysis lets you reason about performance across real-world scenarios, not just theoretical maximums. When Each Case Matters Worst case matters when failures are catastrophic or when you have SLAs to meet. If your pipeline must finish in under 4 hours every night, you need worst-case guarantees, not average-case hopes. Average case matters when you care about throughput over many runs. Best case is useful for detecting when optimizations for common pattern