  In using data assimilation to import information from observations to
estimate parameters and state variables of a model, one must assume a
distribution for the noise in the measurements and in the model errors. Using
the path integral formulation of data assimilation~ cite{abar2009}, we
introduce the idea of self consistency of the distribution of stochastic model
errors: the distribution of model errors from the path integral with observed
data should be consistent with the assumption made in formulating the the path
integral. The path integral setting for data assimilation is discussed to
provide the setting for the consistency test. Using two examples drawn from the
1996 Lorenz model, for $D = 100$ and for $D = 20$ we show how one can test for
this inconsistency with essential no additional effort than that expended in
extracting answers to interesting questions from data assimilation itself.
\end{abstract}
