Statistical Engineering: An Idea Whose Time Has Come?

Several authors, including the American Statistical Association (ASA) guidelines for undergraduate statistics education (American Statistical Association Undergraduate Guidelines Workgroup) American Statistical Association Undergraduate Guidelines Workgroup (2014), “2014 Curriculum Guidelines for Undergraduate Programs in Statistical Science,” Alexandria, VA: American Statistical Asociation. Available at http://www.amstat.org/education/curriculumguidelines.cfm [Google Scholar], have noted the challenges facing statisticians when attacking large, complex, and unstructured problems, as opposed to well-defined textbook problems. Clearly, the standard paradigm of selecting the one “correct” statistical method for such problems is not sufficient; a new paradigm is needed. Statistical engineering has been proposed as a discipline that can provide a viable paradigm to attack such problems, used in conjunction with sound statistical science. Of course, to develop as a true discipline, statistical engineering must be clearly defined and articulated. Further, a well-developed underlying theory is needed, one that would prove helpful in addressing such large, complex, and unstructured problems. The purpose of this expository article is to more clearly articulate the current state of statistical engineering, and make a case for why it merits further study by the profession as a means of addressing such problems. We conclude with a “call to action.”