The span describes the entire space. It's a set of vectors that you can combine using addition and multiplication in order to obtain any other vector in the space. For example a spanning set over the real number plane would be {(1,0), (0,1)}. This particular set is also an orthonormal basis and you can think of each vector as representing two orthogonal dimensions. This is because their dot product is 0.

However, any set of two vectors that are not on the same line will span the real number plane. For example, {(1,1), (0,1)} spans the real number plane, but they are not orthogonal.

Overall though it is always important to be aware of your input space, and the features/dimensions that you use to represent it. You can easily introduce bias or just noise in a number of ways if you aren't thorough. One example would be not normalizing your data.