Identifiability analysis is a group of methods found in mathematical statistics that are used to determine how well the parameters of a model are estimated by the quantity and quality of experimental data.
To address these issues the identifiability analysis could be applied as an important step to ensure correct choice of model, and sufficient amount of experimental data.
The purpose of this analysis is either a quantified proof of correct model choice and integrality of experimental data acquired or such analysis can serve as an instrument for the detection of non-identifiable and sloppy parameters, helping planning the experiments and in building and improvement of the model at the early stages.
Structural methods are also referred to as a priori, because non-identifiability analysis in this case could also be performed prior to the calculation of the fitting score functions, by exploring the number degrees of freedom (statistics) for the model and the number of independent experimental conditions to be varied.
Practical identifiability analysis can be performed by exploring the fit of existing model to experimental data.