In his post “data analysis is thinking, data analysis is theorizing” Sanjay Srivastava touches on several topics that resonated with my sense of the importance of the work that my colleagues and I do in the field of methodology. Sanjay discusses the Many Analysts, One Dataset study, which found that even with the same dataset and same hypotheses, different analysts came to different statistical conclusions. Sanjay states: “the variability was neither statistical noise nor human error. Rather the differences in results were because of different reasoned decisions by experienced data analysts” This well-articulated point highlights some of the important theoretical work that is done by statisticians and methodologists. It also highlights what I believe is so wrong and broken in the relationship between content researchers (what I am using to refer to those researchers who primarily study an academic area such as personality, family relationships, or poverty) and statisticians or data analysts.
An applied statistician is essentially always working outside of their area of expertise. Working outside of your area of expertise is not fun and is not easy. Imagine if to get your summer salary covered you were asked to write two papers in a completely new field. Do you study social psychology? Congratulations, this summer you’ve been assigned to collect data and write two papers about the diversity of insect species in the wetlands of Florida. It’s ludicrous. You wouldn’t even know where to start. This is similar to what you ask statisticians to do when they are involved in a project at the last minute. You essentially say: “Here’s an area of work I’ve spent the last 10 years of my life thinking about, can you tell me if my intervention works by the end of the month?”. Yes, I absolutely can, but if this is the first time you’ve talked to me, I can pretty much guarantee you’re not going to like my answer. All of the underlying groundwork; hundreds of methodological choices have been made without my input. Making decisions at this point is the equivalent of dropping me in a Florida wetland to count bugs. I am going to do it wrong; make the wrong choices, otherwise invalidate your best intentions.
My goal here is not to scare you. Rather my goal is to encourage you. To tell you that science can be better. That you can get closer to answering the questions you really want to answer with a little more forethought. When you want to design a study, you should reach out to someone with expertise in how to design a study. My call to content researchers is to consider the methodologist as a research partner with an area of expertise, and not as someone who can provide some last minute help to run a model. Statisticians will make the wrong choices, and will analyze your data wrong if they don’t understand the underlying theories behind what you’re trying to do, and what new knowledge you’re trying to bring into the world.