Determines how well the model's quantitative outputs match real-world data, considering factors like variability and uncertainty.
Predictive validation is used to compare the model's prediction with actual system behavior.
Employs more rigorous statistical analysis to compare the model's outputs with the statistical distribution of real-world data. This might involve comparing measures like mean, standard deviation, or other relevant statistics.
In a weather forecast model, predictive output validation compares the model's predicted temperature and precipitation amounts with the actual recorded values, assessing how accurate the specific quantitative predictions are.