The concept of “fit for purpose”
The ultimate aim of a laboratory analysis is to produce reliable enough, accurate enough results to allow the proper use of them. We do not undertake testing just for fun or for our own sake. Proper handling of the method validation and verification processes become important. And, the concept of “fit for purpose” sums up what is required.
Indeed, the quality of the analytical chemistry needs to be sufficient to answer the question on the actual situation based on sample analysis. The data user wants to know if he can eat the vegetables safely, drink the water without harm, or invest in the gold mine. Erroneous results can lead to loss of customer confidence.
In order to deliver test results that are “fit for purpose”, a proper understanding of basic statistical data analysis is essential. Unfortunately many laboratory analysts are somehow quite weak in this important subject.
To obtain valid results, we can refer to the six principles of valid analytical measurement (VAM), as proposed by the UK Laboratory of the Government Chemist (LGC):
- Analytical measurement should be made to satisfy an agreed customer requirement
- Use validated methods and equipment
- Use qualified and competent staff to undertake the task
- Participate regularly in independent assessment of technical performance (i.e. proficiency testing)
- Ensure comparability with measurement made in other laboratories (i.e. traceability, reproducibility and measurement uncertainty)
- The laboratory should have well-defined quality control and quality assurance practices.
In selecting random samples for analysis, it is necessary to generate random numbers. Random numbers also are used for simulations and can be used to create sample datasets. Random numbers can be generated in a number of different ways ……
Randomization – Part II
We have been talking about the importance of carrying out random sampling for laboratory analysis. What is actually randomization?
Randomization – Part I
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 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
Linear regression is used to establish a relationship between two variables. In analytical chemistry, linear regression is commonly used in the construction of calibration curve for analytical instruments in, for example, gas and liquid chromatographic and many other spectrophotometric analyses….
A linear regression approach to bias between methods – Part I
Measurement uncertainty has two main contributors, namely sampling uncertainty and analytical uncertainty, but most laboratory analysts tend to equate analytical uncertainty as its measurement uncertainty based on the sample received. This may be true when the target (population) lot sampled is homogeneous where every part of the target have an equal chance of being incorporated in the sample…..
Estimation of bias between 2 sampling methods