Raghu Kacker et al 2006 Metrologia 43 S167 doi:10.1088/0026-1394/43/4/S02
Raghu Kacker1, Blaza Toman1 and Ding Huang2
Show affiliationsWe compare three approaches for quantifying uncertainty through a measurement equation: the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), draft GUM Supplement 1 and Bayesian statistics. For illustration, we use a measurement equation for simple linear calibration that includes both Type A and Type B input variables. We consider three scenarios: (i) the measurement equation is linear with one Type B input variable having a normal distribution, (ii) the measurement equation is non-linear with two Type B input variables each having a normal distribution and (iii) the measurement equation is non-linear with two Type B input variables each having a rectangular distribution. We consider both small and large uncertainties for the Type B input variables. We use each of the three approaches to quantify the uncertainty in measurement for each of the three scenarios. Then we discuss the merits and limitations of each approach.
06.20.fb Standards and calibration
06.20.Dk Measurement and error theory
02.50.-r Probability theory, stochastic processes, and statistics
Issue 4 (August 2006)
Received 24 October 2005
Published 4 August 2006
Raghu Kacker et al 2006 Metrologia 43 S167
B.F. McMillan and R.L. Dewar 2006 Nucl. Fusion 46 477
P Connes 1986 J. Opt. 17 5
Golam Mortuza Hossain 2004 Class. Quantum Grav. 21 179
M Martínez-Búrdalo et al 2004 Phys. Med. Biol. 49 345
R Betti and R B Testa 1995 Smart Mater. Struct. 4 A91
J S Forrest 1953 Br. J. Appl. Phys. 4 S37
B P Lee 1994 J. Phys. A: Math. Gen. 27 2633
P R Charlton et al 2002 Class. Quantum Grav. 19 1493
T Bartsch and T Uzer 2005 J. Phys. B: At. Mol. Opt. Phys. 38 S241