In 2013, the Committee of Presidents of Statistical Societies (COPSS) celebrates its 50th Anniversary. As part of its celebration, COPSS will publish a book, with contributions from past recipients of its awards, titled “Past, Present and Future of Statistical Science”. Below is my contribution titled The bright future of applied statistics. When I was asked to contribute to this issue, titled Past, Present, and Future of Statistical Science, I contemplated my career while deciding what to write about.
It seems like half of the battle in statistics is identifying an important/unsolved problem. In math, this is easy, they have a list. So why is it harder for statistics? Since I have to think up projects to work on for my research group, for classes I teach, and for exams we give, I have spent some time thinking about ways that research problems in statistics arise. I borrowed a page out of Roger’s book and made a little diagram to illustrate my ideas (actually I can’t even claim credit, it was Roger’s idea to make the diagram).
I agree with Roger’s latest post: “we need to expand the tent of statistics and include people who are using their statistical training to lead the new science”. I am perhaps a bit more worried than Roger. Specifically, I worry that talented go-getters interested in leading science via data analysis will achieve this without engaging our research community. A quantitatively trained person (engineers , computer scientists, physicists, etc..) with strong computing skills (knows python, C, and shell scripting), that reads, for example, “Elements of Statistical Learning” and learns R, is well on their way.