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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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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