Caveat: In the following, I use "comparison" to mean "check for equality". Floating point numbers lose precision because binary arithmetic doesn't represent de

Caveat: In the following, I use "comparison" to mean "check for equality". Floating point numbers lose precision because binary arithmetic doesn't represent decimal in all cases ( 1/3 is an easy example). It's not hard to get into a situation where you're asking if a number is 0.0 but due to a precision error the number you have is 0.00000000000001 or whatever and it should have been zero. If you're dealing with anything where precision is paramount (i.e. money or high precision machining) you should consider using something else that matches the real work precision you are trying to model. You can, of course, use floats and test differences rather than strict equality (i.e. distance is < 0.01) and that's fine too... if you remember to do it, and account for the potential for small precision differences to propagate throughout your calculations and accumulate into larger errors.