Disappearance of sample space

In probability theory, the "orthodox approach" defines events, probability measure, random variable, etc., in terms of a sample space (often denoted $\Omega$). However, after a certain point, the sample space "disappears" or fades into the background.

At a certain point in most probability courses, the sample space is rarely mentioned anymore and we work directly with random variables. But you should keep in mind that the sample space is really there, lurking in the background.

Warning! We defined random variables to be mappings from a sample space $\Omega$ to $\mathbb {R}$ but we did not mention the sample space in any of the distributions above. As I mentioned earlier, the sample space often "disappears" but it is really there in the background. Let's construct a sample space explicitly for a Bernoulli random variable. Let $\Omega =[0,1]$ and define $\mathbb {P}$ to satisfy $\mathbb {P} ([a,b])=b-a$ for $0\leq a\leq b\leq 1$. Fix $p\in [0,1]$ and define

$$X(\omega )={\begin{cases}1&\omega \leq p\\0&\omega >p.\end{cases}}$$

Then $\mathbb {P} (X=1)=\mathbb {P} (\omega \leq p)=\mathbb {P} ([0,p])=p$ and $P(X=0)=1-p$. Thus, $X\sim \mathrm {Bernoulli} (p)$. We could do this for all the distributions defined above. In practice, we think of a random variable like a random number but formally it is a mapping defined on some sample space.

See also

• Summary table of probability terms
• List of sample space perspectives