N03: Hands-on Workshop on Assessing and Reporting Measurement Uncertainty – 2 Day

N03: Hands-on Workshop on Assessing and Reporting Measurement Uncertainty – 2 Day

  • Start Date : 3 February, 2026
  • Start Time : 8:00am
  • End Date : 4 February, 2026
  • End Time : 5:00pm
  • Address : Salon V, N Tower

Abstract

This NIST short course covers the propagation of measurement uncertainty using the methods outlined in the JCGM Guide to the Expression of Uncertainty in Measurement from a statistical perspective. The short course will provide participants with a working knowledge of the computational methods needed to assess measurement uncertainty, hands-on experience in the application of these methods, and scientific and statistical insight into the interpretation of the results.

The Hands-on Workshop on Assessing and Reporting Measurement Uncertainty is a 2-day course that will be held at the Measurement Science Conference in Anaheim, CA. The course consists of lectures, short exercises, and hands-on applications covering many aspects of the propagation of uncertainty using examples from NIST work.

The exercises and hands-on applications will use functions for uncertainty analysis from the software package, metRology, written for the open-source R statistical computing environment, as well as the web-based NIST Uncertainty Machine (NUM). Participants should bring their own laptops, if possible. A laptop for use during the short course can be provided (sharing may be required). If you would like to borrow a laptop, please let one of the instructors know as soon as possible. All software except Microsoft Excel is free.

Topics Covered

  • Importance of uncertainty analysis
  • Different statistical approaches and tools for uncertainty analysis
  • Measurement functions
  • Type A and Type B methods for evaluating standard uncertainties
  • Degrees of freedom
  • Sensitivity coefficients
  • Propagation of standard uncertainties
  • Monte Carlo simulation
  • Expanded uncertainties
  • Interpretation and reporting of results

Contact: Jack Prothero (jack.prothero@nist.gov) & Cait Berry (caitlin.berry@nist.gov)

Caitlin Berry - MSC

Caitlin Berry

Caitlin M. Berry received a B.S. in Mathematics from the University of Arizona in 2012, an M.A. in Secondary Mathematics Education from CUNY-City College in 2013, and, following a career in Mathematics Education, a Ph.D. in Applied Mathematics from the University of Colorado-Boulder in 2024. She joined the Statistical Engineering Division at the National Institute of Standards and Technology as a Mathematical Statistician in 2024. Her work focuses on uncertainty quantification, time series and spatial data applications, and the design and analysis of experiments.

Jack Prothero - MSC

Jack Prothero

Jack B. Prothero received a B.S. in Mathematics from the College of William & Mary in 2016 and a Ph.D. in Statistics & Operations Research from the University of North Carolina Chapel Hill in 2021. He recently joined the Statistical Engineering Division at the National Institute of Standards and Technology as a mathematical statistician. His research interests include object-oriented data analysis, statistical methodology, and data integration.