Why I support statisticians and their resistance to hype28 Oct 2014
Despite Statistics being the most mature data related discipline, statisticians have not fared well in terms of being selected for funding or leadership positions in the new initiatives brought about by the increasing interest in data. Just to give one example (Jeff and Terry Speed give many more) the White House Big Data Partners Workshop had 19 members of which 0 were statisticians. The statistical community is clearly worried about this predicament and there is widespread consensus that we need to be better at marketing. Although I agree that only good can come from better communicating what we do, it is also important to continue doing one of the things we do best: resisting the hype and being realistic about data.
This week, after reading Mike Jordan’s reddit ask me anything, I was reminded of exactly how much I admire this quality in statisticians. From reading the interview one learns about instances where hype has led to confusion, how getting past this confusion helps us better understand and consequently appreciate the importance of his field. For the past 30 years, Mike Jordan has been one of the most prolific academics working in the areas that today are receiving increased attention_._ Yet, you won’t find a hyped-up press release coming out of his lab. In fact when a journalist tried to hype up Jordan’s critique of hype, Jordan called out the author.
Assessing the current situation with data initiatives it is hard not to conclude that hype is being rewarded. Many statisticians have come to the sad realization that by being cautious and skeptical, we may be losing out on funding possibilities and leadership roles. However, I remain very much upbeat about our discipline. First, being skeptical and cautious has actually led to many important contributions. An important example is how randomized controlled experiments changed how medical procedures are evaluated. A more recent one is the concept of FDR, which helps control false discoveries in, for example, high-throughput experiments. Second, many of us continue to work in the interface with real world applications placing us in a good position to make relevant contributions. Third, despite the failures alluded to above, we continue to successfully find ways to fund our work. Although resisting the hype has cost us in the short term, we will continue to produce methods that will be useful in the long term, as we have been doing for decades. Our methods will still be used when today’s hyped up press releases are long forgotten.