Mar 6, 2019 to Mar 7, 2019

Learn the principles and practice of estimating measurement uncertainty
Whenever a measurement is made there will always be some uncertainty about the result due to unavoidable errors in the measurement process. Knowledge of this uncertainty allows a judgement to be made as to whether the data are likely to be ‘fit for purpose’. For example, when determining whether a limit has been exceeded, a meaningful interpretation of the results can only be achieved if the uncertainty is known. The evaluation of the uncertainty associated with measurement results is a requirement for testing laboratories accredited to ISO/IEC 17025. This course provides a practical approach to evaluating uncertainty in testing laboratories which is in line with the ISO principles for uncertainty estimation and current accreditation requirements.

The course assumes no prior knowledge of uncertainty evaluation and includes laptop-based workshops using Excel.  For more information call +44 (0)20 8943 7631 or email, quoting TRMU78.

This 2 day course is aimed at analysts who have limited knowledge of measurement uncertainty but need to be able to  evaluate the uncertainty associated with their results.

This course will help you:

  • Understand how uncertainty can be evaluated for chemical test results 
  • Use method validation and quality control data in uncertainty estimates
  • Give your customers confidence in your results
  • Determine the fitness for purpose of your results
  • Demonstrate compliance with regulatory limits and contract specifications 
  • Make valid comparisons between results obtained at different times and places
  • Meet ISO/IEC 17025 accreditation requirements
  • Apply statistical principles through laptopbased workshops.

The course will cover:

Day 1 – The principles

  • Introduction to the concept of measurement uncertainty
  • Statistics for measurement uncertainty estimation
  • Basic principles of evaluating uncertainty: converting and combining uncertainties
  • Cause and effect analysis
  • Quantifying uncertainty components
  • Approaches to uncertainty estimation.

Day 2 – The practice

  • Evaluation of an uncertainty budget using spreadsheets
  • Using data from validation studies
  • Dealing with data from recovery estimations
  • Using precision data from validation studies
  • Handling uncertainty for large concentration ranges
  • Using and conveying uncertainty estimates.
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