@chrisplyler wrote: I still don't understand why the error exists. I get that there are algorithms in place to establish a tolerance. But why is the result spit out by those algorithms adding 0.0000000000000143 to a user input value that is a real number with a lot less significant digits?
Because Fusion 360 represents everything with IEEE floating point. It's not an arbitrary-precision arithmetic system such as, for example, Python. Floating point has many counterintuitive features. For example, it can't represent the value of 0.1 exactly. 0.100000001490116119384765625 is as close as you can come.
Floating point values sometimes look "weird" this way because they're being displayed in base 10. Internally, they're stored in base 2, split into a value portion (the "mantissa") and a scale portion (the exponent). It's a lot like standard scientific or engineering notation, but in binary (e.g., "6.02 x 10^23"). Some things that are nice round numbers in base 10 are repeating decimals when converted to base 2, so they have to be rounded to be stored.
The underlying issue is performance. Arbitrary-precision arithmetic is orders of magnitude slower than IEEE floating point because CPUs and GPUs implement floating point in hardware.