Challenges and Opportunities for Statistics in the Era of Data Science

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?

Authors

Institutions

Publication Details

Journal
Harvard Data Science Review
Published
2025-04-21
DOI
https://doi.org/10.1162/99608f92.abf14c9d
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Challenges and Opportunities for Statistics in the Era of Data Science

Johannes Schmidt-Hieber, Holger Rauhut, Li‐Xuan Qin, Thomas Hotz et al.
1 citations
Harvard Data Science Review
Big Data and Business Intelligence
2.28
article

Challenges and Opportunities for Statistics in the Era of Data Science

Johannes Schmidt-Hieber, Holger Rauhut, Li‐Xuan Qin, Thomas Hotz, Adalbert Wilhelm, Ostap Okhrin, Harald Binder, Sonja Greven, Johannes Lederer, Hernando Ombao, Dimitris N. Politis, Philipp Doebler, Werner Brannath, Alicia Nieto-Reyes, Axel Gandy, Oliver Y. Feng, Ines Wilms, Florian Pein, Ivor Cribben, Joscha Krause, Holger Dette, Stefan Harmeling, Henry W. J. Reeve, Göran Kauermann, Johan Segers, Georg Krempl, Claudia Kirch, Tom Rainforth, David Salinas, Michal Pešta, Myra Spiliopoulou, Barbara Hammer, Yi Yu, Soumendra N. Lahiri, Clayton Scott
article en
1 citations

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?

Harvard Data Science ReviewVol. 7(2)
Technische Universität Ilmenau (DE), Universidad de Cantabria (ES), Memorial Sloan Kettering Cancer Center (US), University of Alberta (CA), Universität Hamburg (DE), University of Freiburg (DE), University of Bremen (DE), Constructor University (DE), Utrecht University (NL), TU Dortmund University (DE), Washington University in St. Louis (US), Charles University (CZ), University of Michigan (US), Maastricht University (NL), University of Bristol (GB), University of California San Diego (US), Humboldt-Universität zu Berlin (DE), University of Warwick (GB), University of Oxford (GB), Michigan United (US), Imperial College London (GB), University of Bath (GB), Lancaster University (GB), King Abdullah University of Science and Technology (SA), Technische Universität Dresden (DE), Ludwig-Maximilians-Universität München (DE), Universität Trier (DE), Ruhr University Bochum (DE), University of Twente (NL), Otto-von-Guericke-Universität Magdeburg (DE), KU Leuven (BE)
Openalex Percentile: Top 13%
Big Data and Business Intelligence
2.28
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.