Distributions
When an abstract population is imagined to underlie a data set, it often contains an infinite number of values. For example, consider the lifetimes of a sample of light bulbs. The population of possible failure times contains all values greater than zero, and this includes an infinite number of values. Moreover, some of these possible values will be more likely than others.
This kind of underlying population is called a distribution.
Positions of cow in a field
Consider the positions of a cow in a field at 6 different times where all locations are equally likely.
The population here contains all possible positions and is therefore infinite.
The idea of a distribution also allows for some possible values to be more likely than others — the cow may be more likely to be in some particular part of the field.