Abstract: Statistics as a scientific discipline is currently facing the great challenge of finding its place in data science
once more. 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 article 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?
