Predictive validation compares a model's predictions to actual observed data to determine how accurately it can predict real-world outcomes. This involves defining validation criteria, collecting real-world data to serve as a comparison, dividing the data into training and validation sets, using the model to make predictions on the validation data and calculating validation metrics to assess the model's performance. Parameter variability and sensitivity analysis are important techniques for understanding how sensitive a model's outputs are to changes in its parameters. This helps evaluate a model's robustness and identify influential parameters.