The statistical optimization approach usually involves various steps like designing the experiments with defined levels and factors, executing the statistically intended experimental trials, assessing the factors of the ideal terms, expecting the responses, analysis of variance (ANOVA), checking model adequacy and validation of the obtained model (Diwaniyan et al. 2012).
What will validation validate?
In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns.
After the method validation, the adequacy of the suggested approach in development of methods for routine pharmaceutical analysis was proven.
Although many other terms have been used – model adequacy, model checking, model validation, model evaluation [3], [5] –, we will use the term goodness-of-fit to refer to this confrontation between statistical model and observed data.
To further validate the adequacy of the proposed model, we also performed external validation with the validated dataset based on the current CHCIS cohort.
The paper has formally introduced the model and discussed results that were compared to the available empirical data and relevant literature in absence of effects from the newly introduced mechanism and it has explored the effects of its introduction, after a face validation of its adequacy.
Hence, the validation tests show the adequacy and suitability of the present DEM MPS method for three-dimensional simulation of solid liquid flows involving free surfaces.
Validation experiments proved the adequacy and accuracy of model, and the results showed the predicted value agreed well with the experimental values.
Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework.
