2DkcTXceO

538 NP-hard inferenceseem to appreciate statistics as a “rocket science” field. Or as a recent blog(August 14, 2013) in Simply Statistics put it: “Statistics/statisticians needbetter marketing” because (among other reasons)“Our top awards don’t get the press they do in other fields. The NobelPrize announcements are an international event. There is alwaysspeculation/intense interest in who will win. There is similar interestaround the Fields Medal in mathematics. But the top award in statistics,the COPSS award, doesn’t get nearly the attention it should. Partof the reason is lack of funding (the Fields is $15K, the COPSS is $1K).But part of the reason is that we, as statisticians, don’t announce it,share it, speculate about it, tell our friends about it, etc. The prestigeof these awards can have a big impact on the visibility of a field.”The fact that there is more public interest in the Fields than in COPSSshould make most statisticians pause. No right mind would downplay thecentrality of mathematics in scientific and societal advancement throughouthuman history. Statistics seems to be starting to enjoy a similar reputationas being at the core of such endeavors as we move deeper into the digital age.However, the attention around top mathematical awards such as the FieldsMedal has hardly been about their direct or even indirect impact on everydaylife, in sharp contrast to our emphasis on the practicality of our profession.Rather, these awards arouse media and public interest by featuring how ingeniousthe awardees are and how difficult the problems they solved, much likehow conquering Everest bestows admiration not because the admirers care oreven know much about Everest itself but because it represents the ultimatephysical feat. In this sense, the biggest winner of the Fields Medal is mathematicsitself: enticing the brightest talent to seek the ultimate intellectualchallenges.And that is the point I want to reflect upon. Have we statisticians adequatelyconveyed to the media and general public the depth and complexityof our beloved subject, in addition to its utility? Have we tried to demonstratethat the field of statistics has problems (e.g., modeling ignorance) that are asintellectually challenging as the Goldbach conjecture or Riemann Hypothesis,and arguably even more so because our problems cannot be formulated bymathematics alone? In our effort to make statistics as simple as possible forgeneral users, have we also emphasized adequately that reading a couple ofstat books or taking a couple of stat courses does not qualify one to teachstatistics?In recent years I have written about making statistics as easy to learnas possible. But my emphasis (Meng, 2009b) has been that we must make atremendous collective effort to change the perception that “Statistics is easyto teach, but hard (and boring) to learn” to a reality of “Statistics is hardto teach, but easy (and fun) to learn.” Statistics is hard to teach because itis intellectually a very demanding subject, and to teach it well requires bothdepth in theory and breadth in application. It is easy and fun to learn because