Technical Brief

The Inefficacy of Chauvenet's Criterion for Elimination of Data Points

[+] Author and Article Information
Braden J. Limb, Dalon G. Work, Joshua Hodson

Mechanical and Aerospace Engineering Department,
Utah State University,
Logan, UT 84322

Barton L. Smith

Mechanical and Aerospace Engineering Department,
Utah State University,
Logan, UT 84322
e-mail: Barton.Smith@usu.edu

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received July 2, 2016; final manuscript received December 26, 2016; published online March 16, 2017. Assoc. Editor: Mark F. Tachie.

J. Fluids Eng 139(5), 054501 (Mar 16, 2017) (3 pages) Paper No: FE-16-1413; doi: 10.1115/1.4035761 History: Received July 02, 2016; Revised December 26, 2016

Chauvenet's criterion is commonly used for rejection of outliers from sample datasets in engineering and physical science research. Measurement and uncertainty textbooks provide conflicting information on how the criterion should be applied and generally do not refer to the original work. This study was undertaken to evaluate the efficacy of Chauvenet's criterion for improving the estimate of the standard deviation of a sample, evaluate the various interpretations on how it is to be applied, and evaluate the impact of removing detected outliers. Monte Carlo simulations using normally distributed random numbers were performed with sample sizes of 5–100,000. The results show that discarding outliers based on Chauvenet's criterion is more likely to have a negative effect on estimates of mean and standard deviation than to have a positive effect. At best, the probability of improving the estimates is around 50%, which only occurs for large sample sizes.

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Bevington, P. R. , and Robinson, D. K. , 2003, Data Reduction and Error Analysis for the Physical Sciences, McGraw-Hill, New York.
Coleman, H. W. , and Steele, W. G. , 2009, Experimentation, Validation, and Uncertainty Analysis for Engineers, Wiley, Hoboken, NJ.
Figliola, R. S. , and Beasley, D. E. , 2010, Theory and Design for Mechanical Measurements, 5th ed., Wiley, Hoboken, NJ.
Holman, J. P. , and Gajda, W. J. , 1989, Experimental Methods for Engineers, McGraw-Hill Education, New York.
Hughes, I. , and Hase, T. , 2010, Measurements and Their Uncertainties: A Practical Guide to Modern Error Analysis, OUP Oxford, New York.
Taylor, J. R. , 1997, An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books, South Orange, NJ.
Young, H. D. , 1996, Statistical Treatment of Experimental Data: An Introduction to Statistical Methods, Waveland Press, Long Grove, IL.
Ross, S. M. , 2003, “ Peirce's Criterion for the Elimination of Suspect Experimental Data,” J. Eng. Technol., 20(2), pp. 38–41.
Kurup, A. L. , Ölçmen, S. M. , and Ahmed, A. , 2015, “ Experimental Study of Co-Annular Jet Subjected to Transverse Disturbances,” Exp. Therm. Fluid Sci., 66, pp. 53–62. [CrossRef]
Griffin, J. , Schultz, T. , Holman, R. , Ukeiley, L. S. , and Cattafesta, L. N., III , 2010, “ Application of Multivariate Outlier Detection to Fluid Velocity Measurements,” Exp. Fluids, 49(1), pp. 305–317. [CrossRef]
Leahy, J. , and Hukins, D. , 2001, “ Viscoelastic Properties of the Nucleus Pulposus of the Intervertebral Disk in Compression,” J. Mater. Sci. Mater. Med., 12(8), pp. 689–692. [CrossRef] [PubMed]
Serkan, K. , 2007, “ Error Data Analyzing of the Integration of the Sensors With Different Measuring Boundaries in Dynamic Environments,” IU-J. Electr. Electron. Eng., 7(2), pp. 439–442.
Phongikaroon, S. , Bezzant, R. W. , and Simpson, M. F. , 2013, “ Measurements and Analysis of Oxygen Bubble Distributions in LiCL–KCL Molten Salt,” Chem. Eng. Res. Des., 91(3), pp. 418–425. [CrossRef]
Ünal, U. O. , 2015, “ Correlation of Frictional Drag and Roughness Length Scale for Transitionally and Fully Rough Turbulent Boundary Layers,” Ocean Eng., 107, pp. 283–298. [CrossRef]
Lopez-Crespo, P. , Moreno, B. , Lopez-Moreno, A. , and Zapatero, J. , 2015, “ Study of Crack Orientation and Fatigue Life Prediction in Biaxial Fatigue With Critical Plane Models,” Eng. Fract. Mech., 136, pp. 115–130. [CrossRef]
Goktan, R. , and Gunes, N. , 2005, “ A Comparative Study of Schmidt Hammer Testing Procedures With Reference to Rock Cutting Machine Performance Prediction,” Int. J. Rock Mech. Min. Sci., 42(3), pp. 466–472. [CrossRef]
Sleeswijk, A. W. , van Oers, L. F. , Guinée, J. B. , Struijs, J. , and Huijbregts, M. A. , 2008, “ Normalisation in Product Life Cycle Assessment: An LCA of the Global and European Economic Systems in the Year 2000,” Sci. Total Environ., 390(1), pp. 227–240. [CrossRef] [PubMed]
Obreshkova, D. P. , Ivanov, K. V. , Tsvetkova, D. D. , and Pankova, S. A. , 2012, “ Quality Control of Aminoacids in Organic Foods and Food Supplements,” Int. J. Pharm. Pharm. Sci., 4(2), pp. 404–409.
Saito, M. , 2007, “ An Application of Finite Field: Design and Implementation of 128-Bit Instruction-Based Fast Pseudorandom Number Generator,” Master's thesis, Hiroshima University, Hiroshima, Japan.
Saito, M. , and Matsumoto, M. , 2008, “ SIMD-Oriented Fast Mersenne Twister: A 128-Bit Pseudorandom Number Generator,” Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, Berlin, pp. 607–622.
Hiroshima University—Department of Mathematics, 2015, “ SIMD-Oriented Fast Mersenne Twister (SFMT),” Hiroshima University, Higashihiroshima, Japan, accessed Jan. 5, 2016, http://www.math.sci.hiroshima-u.ac.jp/∼m-mat/MT/SFMT/
Wichura, M. J. , 1988, “ Algorithm as 241: The Percentage Points of the Normal Distribution,” Appl. Stat., 37(3), pp. 477–484. [CrossRef]


Grahic Jump Location
Fig. 1

The percentage of when removing outliers improved the mean and standard deviation for Chauvenet's criterion




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