Challenges and Opportunities for Statistics in the Era of Data Science

Kirch, Claudia and Lahiri, Soumendra and Binder, Harald and Brannath, Werner and Cribben, Ivor and Dette, Holger and Doebler, Philipp and Feng, Oliver and Gandy, Axel and Greven, Sonja and Hammer, Barbara and Harmeling, Stefan and Hotz, Thomas and Kauermann, Göran and Krause, Joscha and Krempl, Georg and Nieto-Reyes, Alicia and Okhrin, Ostap and Ombao, Hernando and Pein, Florian and Pešta, Michal and Politis, Dimitris and Qin, Li-Xuan and Rainforth, Tom and Rauhut, Holger and Reeve, Henry and Salinas, David and Schmidt-Hieber, Johannes and Scott, Clayton and Segers, Johan and Spiliopoulou, Myra and Wilhelm, Adalbert and Wilms, Ines and Yu, Yi and Lederer, Johannes (2025) Challenges and Opportunities for Statistics in the Era of Data Science. Harvard Data Science Review. (In Press)

Abstract

Statistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. While at the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions, nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory and computational aspects and often driven by other disciplines, such as computer science. However, statistics can—and should—contribute to the advances of data science. Of most interest are the strengths of statistics, such as the mathematical focus that leads to theoretical guarantees. This includes methods for formal modeling, hypothesis tests, uncertainty quantification and statistical inference. Of particular interest are also established statistical frameworks to handle causality or data deficiencies such as dependence, missingness, biases or confounding. This paper summarizes the findings of a discussion workshop on the topic that was held in June 2023 in Hannover, Germany. The discussion centered around the following questions: How must statistics be set up so that it can contribute (more) to modern data science? In which direction should it develop further? Which strengths can already be used now? What conditions must be created so that this can succeed? What can be done to arrive at a common language? What is the added value of formal modeling, inference, and the mathematical perspective taken in statistics?