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Archive for the ‘Measurement uncertainty’ Category

Examples on how to calculate combined standard uncertainty (edited)

Uncertainty calculation

It is very important for anyone interested in the evaluation of measurement uncertainty to fully understand the very basic principles in calculating the combined standard uncertainty.  Let’s look at some worked examples ….

Calculating standard uncertainties for each uncertainty contribution

In evaluating the combined uncertainty of a testing method from various sources of uncertainty, we need to ensure that we work on a platform of standard uncertainties expressed as standard deviations throughout, because in addition to the standard uncertainty (u) values obtained by our own evaluation (Type A uncertainty), we may also encounter the so-called Type B uncertainty contributions which are uncertainty (U) values given by a third party or from experience and other information in different forms.   Read on … How to calculate standard uncertainties for each source of uncertainty


Basic discussion on measurement uncertainty evaluation

MU with error

Currently many measurement uncertainty (MU) courses and workshops for test laboratories in this region are run by metrology experts instead of practicing chemists. Some laboratory analysts and quality control personnel have found the outcome after attending the two- or three-day presentations rather disillusion, leaving the classroom with their minds even more uncertain. This is because they cannot see how to apply in their routine works as there are no practical worked examples demonstrated to satisfy their needs…..  Read on  Measurement uncertainty – the very basic


Estimation of both sampling and measurement uncertainties by Excel ANOVA Data Analysis tool

Sampling and analysis

Estimation of sampling and analytical uncertainties using Excel Data Analysis toolpak

In the previous blog ,  we used the basic ANOVA principles to analyze the total chromium Cr data for the estimation of measurement uncertainty covering both sampling and analytical uncertainties….

A worked example of measurement uncertainty for a non-homogeneous population

Sampling and analysis

A worked example of MU estimation on a non-homogeneous population

For sampling a non-homogeneous target population such as grain cargo, grainy materials or soil, random positions may be selected and split duplicate samples are taken with duplicate laboratory analysis carried out on each sample received.  This approach will be able to address both sampling and analytical uncertainties at the same time…..

A simple example of sampling uncertainty evaluation


A simple example of sampling uncertainty evaluation

In analytical chemistry, the test sample is usually only part of the system for which information is required. It is not possible to analyze numerous samples drawn from a population. Hence, one has to ensure a small number of samples taken are representative and assume that the results of the analysis can be taken as the answer for the whole…..

Improving uncertainty of linear calibration experiments

Standard error of cal curve

Improving uncertainty of linear calibration experiments


Confidence intervals- How many measurements should you take?

Laboratory 1

Confidence intervals – how many measurements to take

The concept of measurement uncertainty – a new perspective

Since the publication of the newly revised ISO/IEC 17025:2017, measurement uncertainty evaluation has expanded its coverage to include sampling uncertainty as well because ISO has recognized that sampling uncertainty can be a serious factor in the final test result obtained from a given sample ……

The concept of measurement uncertainty – a new pespective


A linear regression approach to check bias between methods – Part II


Plot of reference and trial methods

A Worked Example

Suppose that we determined the amount of uranium contents in 14 stream water samples by a well-established laboratory method and a newly-developed hand-held rapid field method…..

A linear regression approach to bias between methods – Part II