A<unk>  is an abstraction of the intuitive concept of chance into the theoretical domains of mathematics, forming the foundations of probability theory and mathematical statistics.
Within probability theory, a<unk>  is defined as a quantity whose values are<unk>  and to which a probability distribution is assigned.  Formally, a<unk>  is a measurable function from a sample space to the measurable space of possible values of the variable.  The formal definition of<unk> s places experiments involving real-valued outcomes firmly within the measure-theoretic framework and allows us to construct distribution functions of real-valued<unk> s.
A<unk>  can be used to describe the process of rolling a fair die and the possible outcomes { 1, 2, 3, 4, 5, 6 }. The most obvious representation is to take this set as the sample space, the probability measure to be uniform measure, and the function to be the identity function.
For a coin toss, a suitable space of possible outcomes is Ω = { H, T } (for heads and tails).  An example<unk>  on this space is
Random variable
Absolute deviation
Degrees of freedom 
Bias of an estimator
Limiting factor
Cognitive bias
Dependent and independent variables
Attributable risk