The radionuclide inventory in radioactive waste from nuclear power plants should be determined to secure the safety of final repositories. As an alternative to time-consuming, labor-intensive, and destructive radiochemical analysis, the indirect scaling factor (SF) method has been used to determine the concentrations of difficult-to-measure radionuclides. Despite its long history, the original SF methodology remains almost unchanged and now needs to be improved for advanced SF implementation. Intense public attention and interest have been strongly directed to the reliability of the procedures and data regarding repository safety since the first operation of the low- and intermediate-level radioactive waste disposal facility in Gyeongju, Korea. In this review, statistical methodologies for SF implementation are described and evaluated to achieve reasonable and advanced decision-making. The first part of this review begins with an overview of the current status of the scaling factor method and global experiences, including some specific statistical issues associated with SF implementation. In addition, this review aims to extend the applicability of SF to the characterization of large quantities of waste from the decommissioning of nuclear facilities.

1. Introduction
2. Current status of SF methodologies
    2.1 Design of experiment
    2.2 Sampling and radiochemical analysis
    2.3 Evaluation of radiochemical data and SFapplicability
    2.4 Determination of SF and the radioactivityof DTM nuclides
3. Global experiences of SF implementation
    3.1 United States of America
    3.2 France
    3.3 Germany
    3.4 Japan
    3.5 Korea
4. Notable potential issues for the appropriatedecision making and evaluationin relation to SF
    4.1 Lack of guidelines for the required samplesize
    4.2 Lack of guidelines for the identificationand treatment of outliers
    4.3 Lack of guidelines for the data at concentrationbelow LOD
    4.4 Speculation on type-II errors and poweranalysis
    4.5 From conventional parametric statisticsinto more advanced data science
5. Conclusions
Acknowledgements
REFERENCES

• Tae-Hyeong Kim(Korea Atomic Energy Research Institute)
• Junghwan Park(Korea Atomic Energy Research Institute, Korea Advanced Institute of Science and Technology)
• Jeongmook Lee(Korea Atomic Energy Research Institute)
• Junhyuck Kim(Korea Atomic Energy Research Institute)
• Jong-Yun Kim(Korea Atomic Energy Research Institute, University of Science and Technology) Corresponding Author
• Sang Ho Lim(Korea Atomic Energy Research Institute, University of Science and Technology) Corresponding Author