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There are many reasons why a perfectly reasonable model may fit a particular data set poorly; for example, independent observation noise (see Figure 4 for an example).
This model may fit poorly, especially when a nonsymmetric response is available.
Thus, the QSS model may fit the majority of data points in this case and give a good approximation.
With large samples, there is considerable power to reject the null hypotheses, even though the model may fit the data well.
Both an effect compartment model and turnover model may fit a given data set equally well, and the choice between the two may need to be made on mechanistic grounds.
Taken together with our findings of S100A4 overexpression in advanced thyroid tumours, the transgenic mouse model may fit well with human PTC, implicating that S100A4 may enhance thyroid cancer invasion and metastasis in cooperation with c-erbB-2/ neu.
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Further, multiple plausible models may fit the data equally well but provide widely varying estimates of the population size.
It is also possible that other families of growth models may fit the pattern of human population size history.
However, as other unexamined models may fit the data as well or better, an accepted model may be merely one that has not yet been invalidated.
Furthermore, most studies select a priori one or, at most, few models for glucocorticoid exposure representation, and do not attempt to investigate if alternative models may fit their data better.
Specifically, we demonstrated that while kinetic models may fit the modal or average value of the unfolding force/time to good approximation, they may dramatically violate the whole distribution of forces.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com