What is the difference between a computer scientist, a statistician, a research psychologist, and a data scientist?1 Certainly, there is some overlap between these disciplines, but each has unique skill sets. A lot to do with the questions that we ask. Statisticians tend to study questions like: How do we calculate degrees of freedom for Mixed Effects Models (link;link). Computer scientists study questions like: How to extract network level properties without traversing an entire network (link;link)? My Ph.D. studies in Experimental Psychology trained me to ask certain questions: What are people like? How does this affect behavior? More broadly: How can we measure this construct? How can we use this data to predict or understand something meaningful?
I believe these different interests are simply a matter of paradigm: ‘tool creation’ vs ‘tool use’. Although I have contributed to statistical software (link), and published methodological work on statistical artifacts of certain personality assessment tools (link), I tend to leave development of statistical techniques and computer programs to much smarter people. Could I write a statistical package to implement a random forest regression model? Probably. It would take me a while, but eventually I could probably do it. Is that the best use of my time? Probably not. Someone else can do it fast and better, and more importantly, it has already been done done (link).
I prefer to apply this awesome machine learning technique to really useful problems: Predicting who is going to default on a personal loan? Calculating the meaning in a Tweet without ever reading it (link)? These application-type questions are the ones that excite me, personally. My PhD advisor, Ryne Sherman, describes this as the ability to operationalize business or theoretical questions to research questions, and in my experience it is a very useful skill to possess.
However, as counterintuitive as it may sound, in my experience the best ‘tool creators’ are not always the best ‘tool users’. Applying these different analytic and statistical techniques techniques requires an understanding of the big picture goals. It also requires a sound understanding or experimental design. It requires not losing the forest for the trees.
1I would like to preface this by saying that, as a personality psychologist by training, I am of course biased in the opinions expressed here.