What is meant by the term "big O notation"?

Big O notation is a mathematical concept used to describe the upper limit of an algorithm's running time or space requirements in relation to the size of the input data. It provides a high-level understanding of algorithm efficiency, allowing developers and computer scientists to categorize algorithms based on how their performance scales with larger input sizes. This notation focuses on the worst-case scenario, which helps predict how an algorithm will behave as the input grows, ultimately allowing for better decision-making when choosing algorithms in software development and data processing.

In practical terms, using big O notation helps in comparing the efficiency of different algorithms, particularly those performing similar tasks. This is crucial for selecting the most appropriate solution in terms of speed and resource consumption. For instance, an algorithm that runs in linear time, expressed as O(n), will generally be more efficient than one that runs in quadratic time, O(n^2), as the input size increases.

Overall, big O notation serves as a foundational concept in computer science for analyzing algorithmic performance, making it easier to understand and communicate how algorithms will scale in real-world applications.