I don't understand why in the case of continuous R.V. (hence, continuous sample spaces) we don't even care about defining a Probability on the sample space.
By analogy with the discrete case (where we define first a probability on the sample space, then an induced one on the event space of the R.V.) we should have defined $P(X\in A)$ as $P(X^{-1}(A))$ (and not $P(X\in A)=\int _A f(t)dt$)!
So, first:
- why don't we care about defining a probability on the sample space?
second:
- Why the probability distribution is defined in the event space of the R.V. and not in the sample space ?
PS: If the probability distribution had been defined on the sample space that would have given the sample space a Probability and then everything would have been perfectly analogous to the discrete case
EDIT: My question is not about understanding what is a probability distribution, nor about defining the $\sigma$-algebra of the events:
it is about understanding why the distribution is defined in the arrival space (aka event space) and not in the departure space (aka sample space).
In other words: why don't we define $f$ as $$P(B)=\int _B f(t)dt$$ for $ B \in \mathscr B(\Omega)$
instead of:
$$P(X\in A)=\int _A f(t)dt$$