ABSTRACT
Let us restate briefly what the actual goal of kinetic modeling is. It is about finding an acceptable model that is able to (1) describe and explain experimental observations, and (2) to make predictions. Therefore, we need to go more in detail concerning the nature of models, to decide what acceptable means, to have a measure on how good (or bad) we can describe measurements, and how precise our predictions are going to be. Many models have been presented in the previous chapters. A real challenge is to choose the right experimental conditions to answer a particular research question, to choose one or more relevant models among the many available, to analyze them properly in conjunction with the data, and to come to a meaningful conclusion. As was stressed in Chapter 2, this process is really of an iterative nature. Statistics is very helpful in going through this cycle in an efficient way. In fact it is indispensable in kinetic modeling, and this chapter provides tools to this end.
