This thesis concerns the testing of three MOS prediction methods exercised by the previous NPS investigations (the Maximum-Probability Method II, the Multiple Linear Regression Method, and the Principal Discriminant Method), plus one additional method (Discriminant Analysis Method), on statistically-derived simulated (i.e., controlled) predictor/predict and data sets for the purpose of determining their relative skills in forecasting a generic weather parameter (predicthand). Of the four methods, three use Bayes Law of Inverse Probability to discriminate, while the other method uses conditional Probability. The simulated data sets, models and observers necessary to accomplish this goal are created according to a uniquely developed simulation design. The results indicate that there is a definite diffusing conditional probability, to forcecast the weather parameter. Through the use of Analysis of Variance (ANOVA) technique, this difference is found to be significant with respect to chance.