The deterministic identifiability of models is only normally considered if a problem becomes apparent in the parameter identification stage of data analysis. If no problem is perceived then the analysis will continue. However, although the problem does not become apparent, the implications of ambiguities in what is inferred from the data should be considered. This paper reviews some fundamentals with respect to model indistinguishability and parameter identifiability.

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