Validating assumptions is crucial for ensuring the reliability of the model. This can be achieved through:
Experimental Data: Comparing model predictions with experimental results. Sensitivity Analysis: Assessing how changes in assumptions affect model outcomes. Peer Review: Subjecting the model to scrutiny by other experts in the field.
