On Probability Space and Random Variables In Statistics, it is very common to see i.i.d random variables $\{X_1, X_2,...,X_n \}$. I wonder whether or  not it is assumed that these random variables come from the same probability space $(\Omega,  \mathcal{F}, P)$, and can you construct i.i.d. random variables from the same probability space?
Another question is that in introductory probability course, you are often asked to compute the probability of the random variable $X$ equal to the random variable $Y$. For example, $X,Y$ are i.i.d random variables distributed according to  $Ber(\theta)$. Do $X$ and $Y$ have to come from the same probability space $(\Omega,  \mathcal{F}, P)$ or not?
Is a new probability space simply assigned whenever a new experiment is conducted? (even if the distributions of random variables are the same in the case of i.i.d random variables)

Henry's answer is good. For the first question, I wanted to point you to the Kolmogorov extension theorem, which gives sufficient conditions under which you can put a collection of random variables into the same probability space. This theorem is good-enough for most situations in introductory probability.
For the second question, certainly you need $X,Y$ in the same probability space to ask for the probability of the event $\{X=Y\}$ (which is shorthand for $\{\omega \in \Omega \, : \, X(\omega) = Y(\omega) \}$).
