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Statistical method validation for test laboratories

An intensive three-day course on statistical method validation for test laboratories

What accreditation LIMS LIS Workshop ISO 17025 laboratory
When 2012-02-28 09:00 to
2012-03-02 17:00
Where Emperor’s Palace, Johannesburg
Contact Name Application Peak Training & Projects
Contact Email
Contact Phone +27 11 402 0110
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Method validation is the process that provides evidence that a given analytical method, when correctly applied produces results that are fit for purpose. No matter how well a method performs elsewhere, analysts need to confirm that the method is valid when applied in their laboratory. There is now much greater emphasis on method validation in the ISO 17025 accreditation standards

The performance of analytical method is primarily measured around precision and accuracy. Laboratories are required to validate the methods they use to ensure that the quality of their tests is accurate and precise. This entails proper and effective management of testing processes meant to authenticate results that come out of such tests

Introduction to basic statistics for test laboratories

Errors vs. uncertainties

Accuracies and precision

Distributions important to test/analytical labs

Samples vs. populations

Confidence limits/intervals, level of confidence

Hypothesis testing

Q-test and G-test for outliers

F-tests, T-tests


Correlations and regression in the analytical test laboratory calibration

Standards

Number of Standards

Inclusion of the blank

Range of Standards

Linearity

Correlation coefficient

Significant linearity – ANOVA

Regression Parameters

Slope, intercept, regression line

Calibration uncertainties

Method sensitivity

Limit of detection – LOD

Limit of quantization – LOQ

Specificity vs. selectivity


Determination

Repeatability uncertainties

Regression uncertainty

Minimization of the regression uncertainty

How to handle dilutions


Method of standard additions


Using Regression to compare the results of two methods/analysts/instruments


Analysis of Variance – ANOVA

Single factor, Two factor – ANOVA

Application in providing the competency of analysts, instruments and method


Control charts

Stewart charts
Range charts
Cusum Charts


Evaluation of the uncertainty of measurement

Identification of uncertainties. Quantifying, combining and extending uncertainties

Typical uncertainties: Mass,  Volume, Purity uncertainties

Repeatability uncertainties


Method validation

Setting validation criteria
Parameters to be validated

Accuracy
Precision
Working range
Linearity
Limit of detection – LOD
Limit of quantization – LOQ
Sensitivity
Selectivity and specificity
Ruggedness
Robustness
Uncertainties
Stability of stock solution
Traceability

 



 

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