By default, mathematical reasoning is understood to take place in a
deterministic mathematical universe. In such a universe, any given mathematical statement
(that is to say, a sentence with no free variables) is either true or false, with no intermediate truth value available. Similarly, any deterministic variable
can take on only one specific value at a time.
However, for a variety of reasons, both within pure mathematics and in the applications of mathematics to other disciplines, it is often desirable to have a rigorous mathematical framework in which one can discuss
non-deterministic statements and variables – that is to say, statements which are not always true or always false, but in some intermediate state, or variables that do not take one particular value or another with definite certainty, but are again in some intermediate state. In probability theory, which is by far the most widely adopted mathematical framework to formally capture the concept of non-determinism, non-deterministic statements are referred to as
events, and non-deterministic variables are referred to as
random variables. In the standard foundations of probability theory, as laid out by Kolmogorov, we can then
model these events and random variables by introducing a
sample space (which will be given the structure of a
probability space) to capture all the ambient sources of randomness; events are then modeled as measurable subsets of this sample space, and random variables are modeled as measurable functions on this sample space.