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Even if the data and observation matches, it may not produce equal results for a different circumstance, thus the model may not be able to predict accurately about the future.

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Rather than being absolute, it is best to view it as Probabilistic

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- The model may appear reasonable but requires testing to indicate if the model is a good model. Thus the model should not be deem as perfect, However, the public may intepret the models as perfect.

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Models can corroborate a hypothesis by offering evidence to strengthen what may be already partly established through other means

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-represents natural systems
-entails loss of inormation at scale lower than averaging scale

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No matter how much data we have, there will always be the possibility that more than one theory can explain the available observations

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Just because the numerical data matches the observation doesn't mean it has been verified. This would be to commit a fallacy

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- the process of tuning  model
- What gives you the best calibration, After that do some validation to prove that the calibration process is right.
- Calibration is to improve the model but over calibration may cause a significant deviation.

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If we compare a result predicted by a model with observational data and the comparison is unfavourable, then we know something is wrong

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- the establishment of legitimacy
- signifies consistecy wihin a system  or between systems
- If the model matches, than the model is reasonable, but not necessarily true.

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-Truth has been demonstrated implying its reliability as a basis for desicion-making.
ONLY POSSIBLE IN CLOSED SYSTEMS
Input parameters are incompletely known

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Models are open