An Assessment of Selected Divisions of the Information Technology Laboratory at the National Institute of Standards and Technology Fiscal Year 2021 (2021) / Chapter Skim
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4 Statistical Engineering Division
4 Statistical Engineering Division
Pages 24-30
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From page 24... ...
Now statistical methodology development is expanding beyond the modeling stage to include the whole data science life cycle including data formulation and data cleaning, with consistent documentation and code repository including the data cleaning process. Obtaining inter-laboratory agreements (necessary for national and international laboratories to reach consensus on metrology issues)
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From page 25... ...
In the area of standing as a neutral and trusted arbiter in areas of controversy, following its longstanding role as the setter of standards in metrology, SED is also seeking to establish standards in other areas of statistical controversy. An illustration of this was the work on the reporting of forensic evidence in court.1 A typical current practice for DNA evidence is to report likelihood ratios to represent evidential weight, but this has become a default practice rather than a carefully reasoned methodology, and as such is quite controversial.
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From page 26... ...
There seems to be excellent synergies occurring between traditional statistics and data science, as reflected in the LANTERN project, which involved a very productive synergy between the two. The incorporation of data science within the existing activities of SED staff could become a challenge if data science were to replace the core statistical elements of SED.
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From page 27... ...
Staff have been engaged with the American Society for Quality, the American Statistical Association, and the International Statistical Engineering Association; NIST hosted the 2019 Fall Technical Conference and participated regularly in the Defense and Aerospace Test and Analysis Workshop (DATAWorks)
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From page 28... ...
Many students, after exposure to the deep scientific collaboration provided by collaborative projects, chose careers at the national laboratories. Intentionally focused engagement with the academic community may enable both recruitment and retention by allowing SED staff to continue their professional development.
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From page 29... ...
A move to increase publication in statistics journals might be achieved by collaborations with academic statisticians to further develop and publish on the novel statistical issues arising from these applications. Additionally, it would be beneficial if SED staff expanded their publication and research presence beyond statistics communities to machine learning journals such as the Journal of Machine Learning Research and conferences such as the Annual Conference on Neural Information Processing Systems (NeurIPS)
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From page 30... ...
It serves as a neutral and trusted arbiter on the interpretation of statistical evidence and the validity of statistical methods. With a relatively small staff size, SED faces a challenge of maintaining core competence in statistical design, modeling, data cleaning, and uncertainty measurement, while at the same time growing new competence to support NIST initiatives in areas such as AI.
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