Statistical Science and Philosophy of Science:

Where Do/Should They Meet in 2010

(and Beyond)?

SOME TENTATIVE QUESTIONS:

 I. The Current Landscape in Philosophical Foundations of Statistics 

·        What is the nature and justification of recent attempts at bridges, reconciliations, and unifications between frequentist and Bayesian statistics? are such unifications possible? desirable? (is "machine learning" a distinct paradigm?) 

·        What are the central problems of frequentist statistics (of various sorts) and Bayesian statistics (subjective, reference, epistemic, other); in what ways do contrasting philosophies of statistics reflect rival conceptions of the nature and role(s) of probability in reaching error-prone inferences? 

·        How are shifts in philosophy of science related to shifts in philosophy of statistics?  How are shifts in statistical techniques and problems related to shifts in foundations?   Are any of the old "statistics wars" relevant?  (Have the wars been won or lost?) 

·        How do contrasting statistical philosophies interconnect with different positions on key statistical principles (e.g., likelihood principle, error statistical principles, stopping rule principles, principles on conditioning) 

II. Foundational Issues in Model Specification, Selection, and Validation 

·        What assumptions underlie popular methods of model selection (e.g., AIC, BIC, HQIC, Autometrics, Probabilistic Reduction)?  Does their leading to different models reflect contrasting underlying statistical philosophies?  Or are the debates largely pragmatic? 

·        In what ways do advances in statistical techniques redefine traditional philosophical problems about induction and scientific discovery?  Can they solve (or merely re-construe) long-standing philosophical problems about induction, learning, discovery?   

·        When should we take account of "data-dependent" model specifications? and if so, how?: can statistical modeling techniques shed light on the puzzles about use-novelty, double-counting, selection effects? 

III. Statistical and substantive inference:  using statistical methods in evidence-based practice and policy (e.g., medicine, economics)  

·        What is the nature of statistical generalization?  how is it related to "substantive" inference, theory testing, confirmation in science? 

·        How are statistical notions of reliability and relevance connected to reliability and relevance more generally? (e.g., relevance of randomized control trials in making inferences "in the wild") 

·        Is learning about reliable patterns and statistical regularities always intermediate to obtaining scientific knowledge, or can it capture a direct goal of importance to science?