Why don't we measure algorithm speed in seconds?
Because execution time in seconds fluctuates based on hardware, background processes, and programming language. Big O measures operations, which is a universal constant.
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More FAQs in What is Big O Notation? An Absolute Beginner's Guide
It measures the growth rate of an algorithm's execution time (or memory usage) relative to the size of the input data.
It stands for 'Order of magnitude', representing the upper bound (worst-case scenario) of the algorithm's complexity.
In software engineering, systems must be designed to survive worst-case data loads without crashing. Optimizing for the worst case guarantees system stability.
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