Big O vs Big $\Theta$ during coding interview Almost every time I see an article about time or space complexity, people are expressing the complexity with Big O, whereas it should be $\Theta$. 
From the book "Cracking the coding interview":


  "In industry (and therefore in interviews), people seem to have merge Θ
   and 
   together. Industry's meaning of big O is closer to what academics mean by
  Θ
  , in that would be seen as incorrect to describe printing an array as $O(n^2)$. Industry would just say this is O(N)"


In an interview context, would it be considered ok to say $\Theta$ instead of O?
If the interviewer is asking : "What's the Big O of this algorithm?", is it alright to answer :"The time complexity of this algorithm is $\Theta$(n)"? 
I'm wondering if most interviewers would think I'm trying to outsmart them by saying that. But I don't feel comfortable by replacing O by $\Theta$ since they don't have the same meaning.
I think its fine. It just shows the interviewer you actually know what the real meaning of big-O and theta are. Just make sure its actually true (the $\Omega$ part) when you have a complicated algorithm and you have used some inequality for big-O complexity proof.