Dear all,

The Open Science Initiative in Statistics (OSIS) at LMU's Department of Statistics, together with the Munich Center for Machine Learning (MCML), is organizing a small, discussion-oriented event on "Epistemic Foundations and Limitations of Statistics and Science - Explaining the Replication Crisis?" on November 22^nd. 

The aim of the event is to bring together statisticians, philosophers of science, and other interested researchers to discuss aspects of Open Science and the replication crisis from an epistemic perspective on the foundations and limitations of statistics (see website for more details).

At a glance

• Who: Statisticians, philosophers of science, and other statistically-methodologically-epistemically interested researchers
• What: Series of talks (ca. 20 minutes + 15 minutes discussion)
• Where: LMU Munich (near main building)
• When: Friday, November 22^nd, 2024

Invited talks (exact titles TBD)

• Sabina Leonelli (TU Munich): "What reproducibility can’t solve"
• Uwe Saint-Mont (HS Nordhausen): "How Feynman Predicted the Replication Crisis"
• Walter Radermacher (Advisory Board on Ethics of the International Statistical Institute and LMU Munich): “Epistemology and Sociology of Quantification based on Convention Theory”
• Michael Schomaker (LMU Munich): “Replicability When Considering Unconditional Interpretations and Gradations of Evidence”
• Rudolf Seising (Deutsches Museum): “An Interwoven History of AI and Statistics”

To join the discussion, please register by October 31^st at https://www.pretix.osc.lmu.de/lmu-osc/osis2024. Seats will be given on a first-register, first-serve basis.

The current program allows some room for further talks – please reach out to epix.workshop@stat.uni-muenchen.de if you would like to add your perspective to the workshop.

We are looking forward to a fruitful exchange on this important topic with experts from across the field!

Kind regards,

Moritz Herrmann, MCML Open Source and Open Data Transfer Coordinator
Patrick Schenk, PhD Candidate Social Data Science and AI Lab, LMU
Lisa Wimmer, PhD Candidate Statistical Learning & Data Science, LMU