A statistical model assumes that data are realisations of random variables. For a parametric model, a set of assumptions are then made about the probability distributions of these random variables.
The simplest assumptions are that the random variables are
Independent and
Identically distributed, according to a probability distribution with unknown parameter values.
Statistical inference involves the estimation of the unknown parameters in the probability distribution.
An estimator of a parameter is a random variable. An estimate of a parameter is a number.
A good estimator should be unbiased and consistent.
The sampling distribution of an estimator displays the distribution of the estimator across repeated samples of a given size.