Big-O notation describes the affect structure growth has on an algorithm's effort.

It is not a measurement.

Big-O is qualitative not quantitative. Big-O is a broad grouping like choosing a T-shirt ("S", "M", "L").

Identifying iterations/recursion helps the analysis.

Use Big-O with other tools such as performance profilers (tools that make time measurements of running code).

Here are the most common Big-O notations (ordered from best to worst in relative efficiency):


Note: recursion would apply similarly to iterations

There are other less common notations such as O(n3) (hopefully rare).