I think you're meaning 'exact' as in they're deterministic. I don't know anyone who knows what they're talking about that argues against this, so I think you're not disagreeing with anyone.
Other people mean 'not exact' as in they can't represent all real numbers. Which is true.
If, however, you meant "the integer datatypes can't represent all integers", then you probably want to say that instead of mixing maths terminology. And you'll also have to back up that claim because bigintegers are as accurate as you have space to store them in; their limitation is not imposed by their spec, but by your hardware.
Unlike IEEE floating point decimal numbers, where the limitation is explicitly part of the spec itself. That limitation is literally why floats even work at all.
There's no need to use a float operation to see this inexact behaviour - floats cannot even represent most reals that humans can write in a computer program. If you write `0.1` in your programming language of choice such that it gets treated as a float, the result is not, in any way, exactly 0.1.
It might be fair to claim that floats are more inexact - certainly moreso than Rational, but probably moreso than BigDecimal for the numbers that programmers tend to care about.
It's not clear what you're getting at here. Are you just saying that each floating-point value precisely represents a real number? Or are you saying that floating-point arithmetic is often exact? [0]
My own pedantic nitpick: the article states that but BigDecimals are [exact]. BigDecimal is only exact in the way floats are exact. BigDecimal has arbitrary precision, but is not able to precisely represent every real. There's no way to come up with a system that would be capable of doing this. No matter how much memory you throw at BigDecimal, you can't precisely represent pi.
[0] Sterbenz Lemma, on which there are surprisingly few good sources. See page 45 of (PDF) http://lyoncalcul.univ-lyon1.fr/ed/DOCS_2012-2013/FloatingPo...
Perhaps it would be clearer to say that floats and floating point operations are deterministic, so you don't have to convince someone that 0.30000000000000004 is the exact answer to 0.1 + 0.2
- they are binary rather than decimal, so rounding behavior doesn’t match what humans expect
- some floating point operations have rounding/error accumulation
You can multiply and divide a float by 2 all day long and every result will be exact, with no error accumulation. However, adding floats together is a different story :-)
That said, decimal types are still much preferable for anything that needs to be “exact” for human usage. It’s very easy to make a mistake with floats, even if they could support your use case.