Oftentimes, data analysis seems like a menu of analyses applied to problems, but lacking an overall structure. Of course, this isn't the case, but it seems that way to programmers without a strong background in statistics.
Frameworks such as confirmatory data analysis and null hypothesis testing provide the structure that may be missing. Generally, when you begin working with data, you start by generating some summary statistics that highlight some of the basic characteristics of the data. Afterwards, you probably generate some graphs that further elucidate the essential qualities of the data. This all falls into the realm of exploratory data analysis.
However, as the exploration wraps up, you'll probably start to think of some theories about the data that you'd like to test. You'll generate some hypotheses, and you'll need to test whether they're true or not. And based on those tests, you'll further refine your knowledge of the data, what's in it, and...
