TY - JOUR
T1 - “So much data. Who needs probability?” Have we been here before?
AU - Shafer, Glenn
N1 - Funding Information:
I am grateful to Peter Song, Professor of Biostatistics at the University of Michigan, for the invitation to present the after-dinner talk at the Fifth Bayesian, Fiducial and Frequentist Conference, and for the good fellowship and encouragement from the audience there. In preparing the talk, I was reminded of my debts towards numerous scholars, especially Steve Stigler, Bernard Bru, Jamie Pietruska, Jeff Goldsmith, Peter Grünwald, Prakash Gorroochurn, and Nell Painter.
Publisher Copyright:
© 2021
PY - 2022/2
Y1 - 2022/2
N2 - Statisticians have been dealing with the paradoxes of ever more data since the French Revolution. The bigger the data, the more we know, and the more we think we know. Do we still need probability to tell us what we don't know? And what ever made us think, in the first place, that statistics needs probability? This text is adapted from an after-dinner talk given at the Fifth Bayesian, Fiducial, and Frequentist Conference, University of Michigan, May 7, 2018.
AB - Statisticians have been dealing with the paradoxes of ever more data since the French Revolution. The bigger the data, the more we know, and the more we think we know. Do we still need probability to tell us what we don't know? And what ever made us think, in the first place, that statistics needs probability? This text is adapted from an after-dinner talk given at the Fifth Bayesian, Fiducial, and Frequentist Conference, University of Michigan, May 7, 2018.
KW - Data science
KW - History of statistics
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U2 - 10.1016/j.ijar.2021.12.016
DO - 10.1016/j.ijar.2021.12.016
M3 - Article
AN - SCOPUS:85121931568
SN - 0888-613X
VL - 141
SP - 183
EP - 189
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
ER -