As we saw in Chapter 3, mechanistic models are more powerful than statistical models since they tell you about the underlying processes and driving patterns, and they are more likely to work correctly when extrapolating beyond the observed conditions.
Brevi manu, when appropriately used, the mentioned earlier eight methodological notions offer deeper insights and facilitate the solution of difficult problems. Although intuition and common sense will be of paramount importance for such a task, sophisticated mathematics justify their use by yielding useful and sometimes quite unobvious results. After all, what counts is a correct CTDA, and investigators should never view a correct analysis as unnecessarily theoretical and complicated.
It is generally appreciated that while the two spatial dimensions of a natural attribute are relatively manageable, their combination with time results in a number of challenges. Among other developments, these challenges have motivated the development of the random field theory.
