I thought it was well known among programmers that (at least using IEEE floating point representation) half the set or storable floats are between -1.0 and 1.0, 1/4 are between -inf and -1.0 and another quarter are between 1.0 and inf.
One practical application of this is: if you have, say a percentage value, and fixed point is out of the question, and you want to retain as much precision as possible and still use floats, don't store it in a float with range [0.0,100.0]. Store it with the range [0.0,1.0]. Also why if you're dealing with angles, you should not store them in the range [0.0,360.0), but instead store them either as radians [0-2π), or better: [-π,π), or store them as [-1.0,1.0) and use trig routines designed to work with that range.
I always thought this made intuitive sense when you understand how the number is encoded. Then again, when I learned programming, we weren't "allowed" to use floats until we demonstrated that we understood how they were represented in memory.
One practical application of this is: if you have, say a percentage value, and fixed point is out of the question, and you want to retain as much precision as possible and still use floats, don't store it in a float with range [0.0,100.0]. Store it with the range [0.0,1.0]. Also why if you're dealing with angles, you should not store them in the range [0.0,360.0), but instead store them either as radians [0-2π), or better: [-π,π), or store them as [-1.0,1.0) and use trig routines designed to work with that range.
I always thought this made intuitive sense when you understand how the number is encoded. Then again, when I learned programming, we weren't "allowed" to use floats until we demonstrated that we understood how they were represented in memory.