  We propose a new general version of Stein's method for univariate
distributions. In particular we propose a canonical definition of the Stein
operator of a probability distribution {which is based on a linear difference
or differential-type operator}. The resulting Stein identity highlights the
unifying theme behind the literature on Stein's method (both for continuous and
discrete distributions). Viewing the Stein operator as an operator acting on
pairs of functions, we provide an extensive toolkit for distributional
comparisons. Several abstract approximation theorems are provided. Our approach
is illustrated for comparison of several pairs of distributions : normal vs
normal, sums of independent Rademacher vs normal, normal vs Student, and
maximum of random variables vs exponential, Frechet and Gumbel.
