We make concrete various \cite{bib:cb,bib:gs,bib:berg} ideas regarding
autocorrelation of stationary Markov processes, with the particular goal of
placing error bars on sample means.  We focus on processes where the
autocorrelation takes the form of a single exponential.  We define a particular
toy-model process, the \emphidx{correlated-uniform Markov process}, which is
exactly solvable.  (This is in contrast to the typical Markov chain Monte Carlo
process:  in the MCMC field, one resorts to experimental methods only for
systems which are \emph{not} exactly solvable.)  When a practitioner applies
new methods to an MCMC process which is itself under examination, it can be
difficult to identify computational problems which arise.  Using this toy-model
process, we elucidate strengths and shortcomings of autocorrelation and its
estimators, clearly separating properties of the estimators themselves from the
properties of the particular Markov process.  The policies developed herein
will be used to design and analyze MCMC experiments for the author's doctoral
dissertation.
