Second, when there's a discrepancy between data and models, people have a tendency to distrust the models, but sometimes the problem lies more in the data.
Discrepancies between field data and model predictions mainly concern the position and length of the first break.
Discrepancies between measured data and model predictions can be explained by considering the slight experimental dishomogeneity in fiber orientation, geometry and distribution in the array.
Using the concept of sign consistency, possible places in the network structure that cause observed discrepancies between experimental data and model structure can be identified, and, furthermore, one can identify changes in the network structure (i.e., adding/removal of certain edges) to minimize these inconsistencies [ 64], (Melas et al. 2013, under revision).
Comparison with the IRI-2012 model reveals discrepancies between data and prediction, that are especially prominent during the periods of very low solar activity.
The discrepancy between experimental data and model predictions were significant and were attributed to interaction between the developing cavern and the vessel walls.
In addition, a methodological adaptationist can accept that an apparent discrepancy between data and the predictions of a model of natural selection may be resolved by concluding that the trait is little influenced by natural selection, instead of by concluding that the model is incorrect.
The important methodological implication of empirical adaptationism stems from the causal power that it assigns to natural selection: an apparent discrepancy between data and the predictions of an optimality model should be resolved by rejection of that model and development of a new model.
Independent variables of each block were adjusted for each other, and those that remained significant at 5% (p ≤ 0.05) were retained in the analysis for adjustment in the next model to reduce discrepancy between the data and the model and reach an economic model with relatively few parameters [ 87].
This criterion was used to reduce discrepancy between the data and the model and reach an economic model with relatively few parameters.
