Best Measurement Uncertainty Guide for Beginners

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Estimating measurement uncertainty is becoming common practice in the Metrology industry. To adhere to the requirements of the ISO 17025 standard and the policies of ILAC, laboratories are obligated to report the measurement uncertainty associated with a given measurement result. Laboratory technicians and personnel who are unfamiliar with this task may have a difficult time implementing the practice. However, there is hope; and, it comes from the hard work of others who have previously mastered the task of uncertainty analysis to develop guides and handbooks.

These are my top 5 recommendations. They are ranked in order of difficulty for the reader to comprehend (e.g. 1 is the easiest, 5 is the most difficult). Therefore, I recommend that beginners start from the top and work their way down the list.

Click on the links below to read or download each document. Please send feedback to info@isobudgets.com.

01) NPL MGPG No. 11 – A Beginner’s Guide to Uncertainty of Measurement

02) UKAS M3003 – The Expression of uncertainty and Confidence in Measurement

03) EA 4/02 – Expression of the Uncertainty of Measurement in Calibration

04) BIPM JCGM 100:2008 – Guide to the Expression of Uncertainty in Measurement

05) NIST Technical Note 1297 – Guidelines for the Evaluating and Expressing the Uncertainty of NIST Measurement Results

BONUS: NASA HDBK-8739.19-3 – Measurement Uncertainty Analysis Principles and Methods
This document is not a guide; it is a handbook. It is not as recognized as the previous five documents, but it should be. This is a great reference handbook to have after reading the previous five documents. I highly recommend it.

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About the Author

Richard Hogan

Richard Hogan is the CEO of ISO Budgets, L.L.C., a U.S.-based consulting and data analysis firm. Services include measurement consulting, data analysis, uncertainty budgets, and control charts. Richard is a systems engineer who has laboratory management and quality control experience in the Metrology industry. He specializes in uncertainty analysis, industrial statistics, and process optimization. Richard holds a Masters degree in Engineering from Old Dominion University in Norfolk, VA. Connect with Richard on Google+ and LinkedIn.

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