If you have n^7 and 5(n^6 + 7n^2), then for every n >= 6, the first is larger. In general, for every pair of polynomials f and g of max degrees s and t respectively, and f will eventually dominate g if and only if s > t. Constant multiples are taken into account in big-O, but in a sense they can only delay the inevitable - for any nonzero constant multiples you pick to multiply f and g by, there always will exist a point n such that after that point, f dominates g forever.
It might be that that n, however, is too large for your purposes - perhaps that's what you mean by optimism. Sedgewick has written about other types of analysis that take this into account, see Algorithms for the Masses: http://www.cs.princeton.edu/~rs/talks/AlgsMasses.pdf .
You're probably thinking of not worst-case analysis (which is what is typically done for Big O), but average-case analysis. That is, you're not asking about what is the worst possible running time, but on average, what will the typical running time be? That requires a more complicated analysis. Specifically, we have to start giving probability distributions to our inputs. In worst-case analysis, reasoning about the inputs is easy: just figure out what inputs will give us the worst performance, no matter how rare those inputs may be. In average-case analysis, we have to start figuring out what the likely inputs will be. That requires more work and more assumptions. Typically, you do average-case analysis with a particular problem domain in mind - otherwise, you can't reason about what kinds of inputs are frequent or rare.
Apart from worst-case complexity, they would also analyse average complexity, amortized complexity, etc.
I do understand that. My point was that the analysis is likely too optimistic in some cases. Maybe that's not an issue. I guess the whole thing is fairly arbitrary anyway, and conservative, since we're going to use "worst case" wherever it can be applied.