## 9.1 Principles of Interpretation

There are several principles of interpreting results that we will illustrate in this chapter. These principles are:

1. Revisit your original question

2. Start with the primary statistical model to get your bearings and focus on the nature of the result rather than on a binary assessment of the result (e.g. statistically significant or not). The nature of the result includes three characteristics: its directionality, magnitude, and uncertainty. Uncertainty is an assessment of how likely the result was obtained by chance.

3. Develop an overall interpretation based on (a) the totality of your analysis and (b) the context of what is already known about the subject matter.

4. Consider the implications, which will guide you in determining what action(s), if any, should be taken as a result of the answer to your question.

It is important to note that the epicycle of analysis also applies to interpretation. At each of the steps of interpretation, you should have expectations prior to performing the step, and then see if the result of the step matches your expectations. Your expectations are based on what you learned in the process of your exploratory data analysis and formal modeling, and when your interpretation doesn’t match your expectations, then you will need to determine whether they don’t match because your expectations are incorrect or your interpretation is incorrect. Even though you may be on one of the last steps of data analysis when you are formally interpreting your results, you may need to go back to exploratory data analysis or modeling to match expectations to data.