What are some common mistakes in Big O notation?

What are some common mistakes people make when using Big O notation?

Some common mistakes include misunderstanding the difference between worst-case and average-case analysis, confusing Big O with Big Theta, and not considering constant factors.

Can you explain the difference between worst-case and average-case analysis?

Sure! Worst-case analysis looks at the maximum time or space required for an algorithm, while average-case analysis considers the expected time or space over all possible inputs.

What about the confusion between Big O and Big Theta?

Big O notation describes an upper bound on the time or space complexity, while Big Theta provides a tight bound, meaning it describes both the upper and lower bounds.

How can I avoid these mistakes in my analysis?

To avoid these mistakes, always clarify the context of your analysis, practice with different algorithms, and consult reliable resources to reinforce your understanding.