ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION
Abstract
The singlesided lower tolerance factors frequently used for nontrending assessment of the validation bias and bias uncertainty are sensitive to departures from normality. When used properly, the tolerance limits ensure that an appropriate fraction of the true population of applicable critical experiments lies above the calculated lower tolerance limit with the required statistical confidence level. One condition necessary to ensure that the appropriate proportion of the true population of keff values in the validation suite lies above the lower tolerance limit is that the assumption that the normality of the underlying population of critical experiments is valid or conservative.This paper discusses various methods used to assess whether the assumption that the validation suite may be treated as a random sample drawn from a normal distribution is acceptable. Techniques for assessing the validity of this underlying assumption include common omnibus hypothesis tests for normality, assessment of sample skewness and kurtosis of the validation suite, and graphical techniques. These techniques are used to assess the nature and potential conservatism/nonconservatism imparted by various departures from normality where possible. A review of hypothesis testing is also presented to frame the discussion of omnibus normality tests. Additionally, two cases are analysed with these techniques tomore »
 Authors:

 ORNL
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1566986
 DOE Contract Number:
 AC0500OR22725
 Resource Type:
 Conference
 Resource Relation:
 Conference: International Conference on Nuclear Criticality Safety (ICNC 2019)  Paris, , France  9/15/2019 8:00:00 AM9/20/2019 4:00:00 AM
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Clarity, Justin B., and Marshall, William B.J. ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION. United States: N. p., 2019.
Web.
Clarity, Justin B., & Marshall, William B.J. ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION. United States.
Clarity, Justin B., and Marshall, William B.J. Sun .
"ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION". United States. https://www.osti.gov/servlets/purl/1566986.
@article{osti_1566986,
title = {ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION},
author = {Clarity, Justin B. and Marshall, William B.J.},
abstractNote = {The singlesided lower tolerance factors frequently used for nontrending assessment of the validation bias and bias uncertainty are sensitive to departures from normality. When used properly, the tolerance limits ensure that an appropriate fraction of the true population of applicable critical experiments lies above the calculated lower tolerance limit with the required statistical confidence level. One condition necessary to ensure that the appropriate proportion of the true population of keff values in the validation suite lies above the lower tolerance limit is that the assumption that the normality of the underlying population of critical experiments is valid or conservative.This paper discusses various methods used to assess whether the assumption that the validation suite may be treated as a random sample drawn from a normal distribution is acceptable. Techniques for assessing the validity of this underlying assumption include common omnibus hypothesis tests for normality, assessment of sample skewness and kurtosis of the validation suite, and graphical techniques. These techniques are used to assess the nature and potential conservatism/nonconservatism imparted by various departures from normality where possible. A review of hypothesis testing is also presented to frame the discussion of omnibus normality tests. Additionally, two cases are analysed with these techniques to provide an example of how they should be implemented.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}