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Challenges and Opportunities for Statistics in the Era of Data Science

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Challenges and Opportunities for Statistics in the Era of Data Science. / Kirch, Claudia; Lahiri, Soumendra; Binder, Harald et al.
In: Harvard Data Science Review, 21.04.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kirch, C, Lahiri, S, Binder, H, Brannath, W, Cribben, I, Dette, H, Doebler, P, Feng, O, Gandy, A, Greven, S, Hammer, B, Harmeling, S, Hotz, T, Kauermann, G, Krause, J, Krempl, G, Nieto-Reyes, A, Okhrin, O, Ombao, H, Pein, F, Pešta, M, Politis, D, Qin, L-X, Rainforth, T, Rauhut, H, Reeve, H, Salinas, D, Schmidt-Hieber, J, Scott, C, Segers, J, Spiliopoulou, M, Wilhelm, A, Wilms, I, Yu, Y & Lederer, J 2025, 'Challenges and Opportunities for Statistics in the Era of Data Science', Harvard Data Science Review. https://doi.org/10.1162/99608f92.abf14c9d

APA

Kirch, C., Lahiri, S., Binder, H., Brannath, W., Cribben, I., Dette, H., Doebler, P., Feng, O., Gandy, A., Greven, S., Hammer, B., Harmeling, S., Hotz, T., Kauermann, G., Krause, J., Krempl, G., Nieto-Reyes, A., Okhrin, O., Ombao, H., ... Lederer, J. (in press). Challenges and Opportunities for Statistics in the Era of Data Science. Harvard Data Science Review. https://doi.org/10.1162/99608f92.abf14c9d

Vancouver

Kirch C, Lahiri S, Binder H, Brannath W, Cribben I, Dette H et al. Challenges and Opportunities for Statistics in the Era of Data Science. Harvard Data Science Review. 2025 Apr 21. doi: 10.1162/99608f92.abf14c9d

Author

Kirch, Claudia ; Lahiri, Soumendra ; Binder, Harald et al. / Challenges and Opportunities for Statistics in the Era of Data Science. In: Harvard Data Science Review. 2025.

Bibtex

@article{70328fba2dc1423a99681a89285fcf75,
title = "Challenges and Opportunities for Statistics in the Era of Data Science",
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?",
author = "Claudia Kirch and Soumendra Lahiri and Harald Binder and Werner Brannath and Ivor Cribben and Holger Dette and Philipp Doebler and Oliver Feng and Axel Gandy and Sonja Greven and Barbara Hammer and Stefan Harmeling and Thomas Hotz and G{\"o}ran Kauermann and Joscha Krause and Georg Krempl and Alicia Nieto-Reyes and Ostap Okhrin and Hernando Ombao and Florian Pein and Michal Pe{\v s}ta and Dimitris Politis and Li-Xuan Qin and Tom Rainforth and Holger Rauhut and Henry Reeve and David Salinas and Johannes Schmidt-Hieber and Clayton Scott and Johan Segers and Myra Spiliopoulou and Adalbert Wilhelm and Ines Wilms and Yi Yu and Johannes Lederer",
year = "2025",
month = apr,
day = "21",
doi = "10.1162/99608f92.abf14c9d",
language = "English",
journal = "Harvard Data Science Review",

}

RIS

TY - JOUR

T1 - Challenges and Opportunities for Statistics in the Era of Data Science

AU - Kirch, Claudia

AU - Lahiri, Soumendra

AU - Binder, Harald

AU - Brannath, Werner

AU - Cribben, Ivor

AU - Dette, Holger

AU - Doebler, Philipp

AU - Feng, Oliver

AU - Gandy, Axel

AU - Greven, Sonja

AU - Hammer, Barbara

AU - Harmeling, Stefan

AU - Hotz, Thomas

AU - Kauermann, Göran

AU - Krause, Joscha

AU - Krempl, Georg

AU - Nieto-Reyes, Alicia

AU - Okhrin, Ostap

AU - Ombao, Hernando

AU - Pein, Florian

AU - Pešta, Michal

AU - Politis, Dimitris

AU - Qin, Li-Xuan

AU - Rainforth, Tom

AU - Rauhut, Holger

AU - Reeve, Henry

AU - Salinas, David

AU - Schmidt-Hieber, Johannes

AU - Scott, Clayton

AU - Segers, Johan

AU - Spiliopoulou, Myra

AU - Wilhelm, Adalbert

AU - Wilms, Ines

AU - Yu, Yi

AU - Lederer, Johannes

PY - 2025/4/21

Y1 - 2025/4/21

N2 - 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?

AB - 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?

U2 - 10.1162/99608f92.abf14c9d

DO - 10.1162/99608f92.abf14c9d

M3 - Journal article

JO - Harvard Data Science Review

JF - Harvard Data Science Review

ER -