This paper describes a new mathematical method called conflation for consolidating data from independent experiments that measure the same physical quantity. Conflation is easy to calculate and visualize and minimizes the maximum loss in Shannon information in consolidating several independent distributions into a single distribution. A formal mathematical treatment of conflation has recently been published. For the benefit of experimenters wishing to use this technique, in this paper we derive the principal basic properties of conflation in the special case of normally distributed (Gaussian) data. Examples of applications to measurements of the fundamental physical constants and in high energy physics are presented, and the conflation operation is generalized to weighted conflation for cases in which the underlying experiments are not uniformly reliable.
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September 2011
Research Article|
July 20 2011
How to combine independent data sets for the same quantity
Theodore P. Hill;
Theodore P. Hill
1
School of Mathematics, Georgia Institute of Technology, Atlanta
, Georgia 30332, USA
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Jack Miller
Jack Miller
2
Lawrence Berkeley National Laboratory, Berkeley
, California 94720, USA
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Chaos 21, 033102 (2011)
Article history
Received:
December 06 2010
Accepted:
April 29 2011
Citation
Theodore P. Hill, Jack Miller; How to combine independent data sets for the same quantity. Chaos 1 September 2011; 21 (3): 033102. https://doi.org/10.1063/1.3593373
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