  Stein's method of exchangeable pairs is examined through five examples in
relation to Poisson and normal distribution approximation. In particular, in
the case where the exchangeable pair is constructed from a reversible Markov
chain, we analyze how modifying the step size of the chain in a natural way
affects the error term in the approximation acquired through Stein's method. It
has been noted for the normal approximation that smaller step sizes may yield
better bounds, and we obtain the first rigorous results that verify this
intuition. For the examples associated to the normal distribution, the bound on
the error is expressed in terms of the spectrum of the underlying chain, a
characteristic of the chain related to convergence rates. The Poisson
approximation using exchangeable pairs is less studied than the normal, but in
the examples presented here the same principles hold.
