Chapter 5 Models and Estimation | Data Analysis in R

One of the most critical concepts in basic statistics is to be aware of the fact that a statistical model and a statistical estimation procedure are two different things that both go into your analysis, and often times can both be manipulated. Too often in introductory statistics training, the focus is on the model—and the estimation is along for the ride. Even if you adopt a single estimation procedure and stick with it, it remains important to learn from the start that you have options. For example, simply because you want to use an ANOVA model does not mean you are required to use a Frequentist estimation procedure. A model and an estimation procedure are like a car and driver—you need both to get where you are going, but changes to either could result in the experience of how you get where you are going or where you end up. Models are the machinery—the car in the previous example—which can be thought of as a description of the system, process, or relationship you are trying to eval

One of the most critical concepts in basic statistics is to be aware of the fact that a statistical model and a statistical estimation procedure are two different things that both go into your analysis, and often times can both be manipulated. Too often in introductory statistics training, the focus is on the model—and the estimation is along for the ride. Even if you adopt a single estimation procedure and stick with it, it remains important to learn from the start that you have options. For example, simply because you want to use an ANOVA model does not mean you are required to use a Frequen