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Date: Tue, 9 Jul 2019 at 23:10

Big-O means “worst” case complexity. We need to be doubly sure what “worst” case data look like. (Note it doesn’t mean ridiculously rare data like integer overflow.)

On high-end coding interviews, these “worst data set” frequently shed light on the structure of the original problem, and often point to the correct direction for an efficient solution. (Note a nice-looking solution is not efficient if it exhibits poor time complexity in the face of a worst yet realistic data set. Such a system may not scale well in practice, given its Achilles’ heel.)

If interviewer wants a solution optimized for bigO time complexity, you can waste precious time tackling the wrong problem. *The wrong problem is the problem defined by your idea of “worst data set”*, but the real worst data set is very different.

Sometimes, the worst data is *_subtly_* different but it points to a different algorithm direction. It may point to an iterative solution rather than recursion, for example.

### The original interview question may not be that hard iFF we identify the worst data set early on, but sadly, we run out of time.

For some tough problems, the first (sometimes the only) challenge is quickly identifying worst data set. Interviewer always gives you the simple data set, NEVER the worst data set. It’s your job to identify the worst data set… often the key insight into the problem.

It’s no exaggeration to say —** identifying the worst data set early or too late can make-or-break your chance at this employer**. You may kiss good-bye to this job opportunity exactly because you are too slow to identify the worst data set. I know what this means. Other candidates probably got shot by this arrow on the heel, without knowing what happened.

Time is ticking as soon as the problem is presented to you. If interviewer says time is not a factor… take your time.. we want quality and thought process … I just ignore it. Our speed is always part of assessment. Therefore, a very common failure scenario is —

.. tractable problem but candidate runs out of time after wasting time on preliminaries like using incorrect worst data set to reason about the (wrong) problem.