Some useful tactics for statisticians solving complex problems

Problem-solving is central to contributions that statisticians and data scientists can make as part of collaborative interdisciplinary teams. Mastering getting up to speed quickly with a problem, identifying the critical aspects of a problem, as well as assessing how statistics can improve the solution are all important aspects of becoming a valued and valuable contributor to solving complex problems. In this article, we explore three general problem-solving tactics that can increase the impact of statisticians and build confidence with tackling messy multi-stage problems. Problem decomposition focuses on breaking large messy problems into manageable pieces. Approximation identifies key components of a complex problem that can help prioritize resources, maximize improvement opportunities improvement and identify areas of weakness. Analogies leverage successful results used in closely or more-distantly related areas that provide ideas or lead to potential paths to solutions. We discuss aspects of these general approaches where statisticians can make unique contributions. A complex problem tackled by a team including the two authors illustrates how the different tactics were used and combined for an enhanced solution. We also share ideas for how teaching and practicing these skills can be incorporated into statistics/data science training and our daily lives.