While the uncertainty related to mapping/quantification of hydrocarbons initially in place is well understood, there are problems regarding the sources and propagation of errors/uncertainties in reservoir simulation. Because reservoir models use measured data from only a small fraction of the total reservoir volume, the challenge is to construct a reservoir model that uses the available data and minimizes errors in simulation results. Several recent studies have aimed at performing a total uncertainty analysis of reservoir simulation results. Underlying such work is usually a number of hypotheses/assumptions that are not always clearly expressed. In this paper, we will discuss implications of some of the statistical methods that are commonly applied in uncertainty analysis and construction of a geological model. The Bayesian approach, where additional data can reduce uncertainties, is emphasized. Several papers have demonstrated large variations in parameters obtained from routine and special core analysis on samples originating from the same geological building block (lithofacies). This variation, which sometimes may be difficult to dissolve from uncertainty in the measurements, must be accounted for in models that describe small scale variation.