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Expand Up @@ -85,10 +85,10 @@ Notice the purple line we added. Now as we add more terms to the array, it takes

## Okay, sure, but why

This sort of analysis is useful for taking a high level view. It's a useful tool for deciding if your designed implementation is going to much the performance profile that you need.
This sort of analysis is useful for taking a high level view. It's a useful tool for deciding if your designed implementation is going to match the performance profile that you need.

A good example would be if we were designing a comment system for a site and it had a sorting and filtering ability. If this is for a school and there would only ever be a few comments at a time, we probably don't need to do too much Big O analysis because it's such a small set of people that a computer can overcome just about any computational inefficiency we have. In this case I'd value human time over computer time and just go with the simplest solution and not worry about the Big O unless performance because a problem later.
A good example would be if we were designing a comment system for a site and it had a sorting and filtering ability. If this is for a school and there would only ever be a few comments at a time, we probably don't need to do too much Big O analysis because it's such a small set of people that a computer can overcome just about any computational inefficiency we have. In this case I'd value human time over computer time and just go with the simplest solution and not worry about the Big O unless performance becomes a problem later.

Okay, now, if we're designing a comment system but it's for Reddit.com, our needs change _dramatically_. We're now talking about pipelines of millions of users making billions of comments. Our performance targets need to change to address such volume. A O(n²) alogrithm would crash the site.

This is absolutely essential to know about Big O analysis. It's useless without context. If you're asked is in a situation if a O(n) or a O(n²) your answer should be "it depends" or "I need more context". If the O(n) algorithm is extremely difficult to comprehend and the O(n²) algorithm is dramatically easier to understand and performance is a total non-issue, then the O(n²) is a much better choice. It's similar to asking a carpenter if they want a hammer or a sledgehammer without any context. Their answer will be "what do you need me to do?".
This is absolutely essential to know about Big O analysis. It's useless without context. If you're asked in a situation if a O(n) or a O(n²) is better your answer should be "it depends" or "I need more context". If the O(n) algorithm is extremely difficult to comprehend and the O(n²) algorithm is dramatically easier to understand and performance is a total non-issue, then the O(n²) is a much better choice. It's similar to asking a carpenter if they want a hammer or a sledgehammer without any context. Their answer will be "what do you need me to do?".