On the relative importance of mathematical abstraction in graduate statistical education

_Editor’s Note: This is the counterpoint in our series of posts on the value of abstraction in graduate education. See Brian’s defense of abstraction on Monday and the comments on his post, as well as the comments on our original teaser post for more. See below for a full description of the T-bone inside joke*._** Brian did a good job at defining abstraction. In a cagey debater’s move, he provided an incredibly broad definition of abstraction that includes the reason we call a a smiley face, the reason why we can apply least squares to a variety of data types, and the reason we write functions when programming.

Statistics is not math...

Statistics depends on math, like a lot of other disciplines (physics, engineering, chemistry, computer science). But just like those other disciplines, statistics is not math; math is just a tool used to solve statistical problems. Unlike those other disciplines, statistics gets lumped in with math in headlines. Whenever people use statistical analysis to solve an interesting problem, the headline reads: “Math can be used to solve amazing problem X” or