View source for Brief Guidelines for Monte Carlo Studies

These guidelines were developed by faculty for doctoral theses and the PhD Written Prelim Exam, but are applicable to any Monte Carlo study.
<ol>
<li>A Monte Carlo study is an experiment whose methodology
should be explained clearly enough that another researcher
can replicate the results of the study.  The goals of the
study should motivate the design of the experiment.</li>
<li>The design of the experiment (e.g., factors, levels,
etc.) dictates the statistical analysis.  Efficient use
of resources usually requires comparing methods with the
same generated data. This blocking factor should be taken
into account in the analysis.</li>
<li>Interpreted results should be justified by proper
statistical comparisons.  Presenting a table of means
and standard errors may enable the reader to do
a conservative, approximate t-test to test equality
of means (assuming positive correlation due to blocking),
but a good exposition shows the details for at
least one example. Details of the analysis should be
included for more complex comparisons.</li>
<li>Graphs can present more information in less space
than tables and should be easier to interpret.  Both
tables and graphs should be as self-explanatory as
possible.  All tables and graphs should include some
indication of their accuracy; usually a summary of
standard errors of entries or plotted points (average,
range, etc.) will suffice.  The number of significant
digits presented in tables should be supported by
their accuracy and should rarely exceed 2 or 3.</li>
</ol>