@holomorphic
@lemmy.world> binom.test(11,n=24, alternative = "two.sided")
Exact binomial test
data: 11 and 24
number of successes = 11, number of trials = 24, p-value = 0.8388
alternative hypothesis: true probability of success is not equal to 0.5
95 percent confidence interval:
0.2555302 0.6717919
sample estimates:
probability of success
0.4583333
Probably not. Or at least we can't conclude that from the data. ¯\_(ツ)_/¯
I have yet to meet a single logician, american or otherwise, who would use the definition without 0.
That said, it seems to depend on the field. I think I've had this discussion with a friend working in analysis.
But the vector space of (all) real functions is a completely different beast from the space of computable functions on finite-precision numbers. If you restrict the equality of these functions to their extension,
defined as f = g iff forall x\in R: f(x)=g(x),
then that vector space appears to be not only finite dimensional, but in fact finite. Otherwise you probably get a countably infinite dimensional vector space indexed by lambda terms (or whatever formalism you prefer.) But nothing like the space which contains vectors like
F_{x_0}(x) := (1 if x = x_0; 0 otherwise)
where x_0 is uncomputable.
Functions from the reals to the reals are an example of a vector space with elements which can not be represented as a list of numbers.