My understanding of probability theory is a rigorous measure-theoretic one and I know almost nothing about descriptive statistics. However, I need to understand some of the commonly used notions in this field.
Say we are interested in the average weight of all people in the world and we are measuring the sizes $x_1,\ldots,x_n$ of $n\in\mathbb N$ "randomly chosen" people.
In this simple example, what do the terms "population", "population distribution", "population mean" and "sample mean" refer to?
My understanding is the following: Let $(\Omega,\mathcal A,\operatorname P)$ be a probability space and $\mu$ be the probability distribution on $(\mathbb R,\mathcal B(\mathbb R))$ describing the distribution of the weights of all people in the world.
Then the experiment described above should simply mean that we are taking independent random variables $X_1,\ldots,X_n:\Omega\to\mathbb R$ with $\operatorname P\circ X_i^{-1}=\mu$ for all $i\in\{1,\ldots,n\}$. And the particular people we have selected correspond to an event $\omega\in\Omega$. So, $x_i=X_i(\omega)$. Now I guess the "population mean" is $\operatorname E[X_1]=\int x\:\mu({\rm d}x)$ and the "sample mean" is simply the unbiased estimator $\frac1n\sum_{i=1}^nX_i$ evaluated at $\omega$.
Did I get these things right? I'm not sure how "population" and "population distribution" are different. It seems like both would refer to the distribution of the random variables, which is $\mu$ here.