ICTD International Centre for Training and Development

Advanced Statistical Analysis of Laboratory Data: Method Development, Method Validation, Uncertainty, Calibration, sqc and Data Interpretation

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About the course

Quantitative chemical measurement has a degree of uncertainty linked to its results which is determined by the performance characteristics of the analytical method used. Measurement uncertainty has often been evaluated on the basis of repeatability and reproducibility of data, but the measurement uncertainty, as expressed in the "Guide to the Expression of Uncertainty in Measurement" published by ISO in 1993, goes further and gives general rules for the evaluation of measurement uncertainty based on both statistical (Type A) and non-statistical (Type B) uncertainties. Method validation is important to evaluate the procedure in the analytical laboratory. The course covers the bulk of the methods and techniques documented in the ISO guide and give a general principle of quantitative analysis and statistical data.

Course Objectives

Upon successful completion of this course, the delegates will be able to:

  • Concepts involved in the calculation of measurement uncertainty
  • Calculating measurement uncertainty in a practical and pragmatic manner
  • Defining measurement processes
  • Identifying sources of measurement error
  • Selecting appropriate error distributions
  • Using different methods to evaluate measurement uncertainty
  • Measurement uncertainty by practice methods
  • Method validation theoretical and practical

Course Outline

Day 1

  • Instrument analysis data
  • Peak evaluation
  • Interpolated graph calibration using external and internal standards
  • Standard addition method extrapolated graph
  • Errors in quantitative analysis
  • Random and systematic errors in titration analysis
  • Standard deviation of repeated measurements
  • Distribution of errors

Day 2

  • Confidence limit of the mean of replicate measurements
  • Measurement uncertainty
  • Errors in instrumental analysis regression and correlation
  • Use of regression lines for comparing analytical methods
  • Confidence limit for X-value
  • Outliers in regression
  • Limit of detection
  • Significance tests for evaluation of experimental results
  • Validation methods
  • Calibration methods

Day 3

  • Data interpretation
  • (T-test) comparison of a mean with a known value
  • (T-test) comparison of the means of two samples with S1»S2
  • (T-test) comparison of the means of two samples with S1 ¹ S2
  • Paired T-test and One-tailed and Two-tailed tests
  • (F-test) for the comparison of standard deviations
  • ANOVA-test analysis of several means and variances
  • Testing for normality of distribution
  • Outliers test
  • Non-parametric or distribution-free methods

Day 4

  • Box and whisker plots
  • Comparison of a median with a known value (the sign test)
  • Confidence interval for non-parametric methods
  • Comparison of the medians of two methods (the sign test)
  • Comparison median of two un-depended samples (Wilcoxon Rank-Sum test)
  • Comparison spread of two sets of non-parametric results (Siegel-Tukey test)
  • Rank Correlation for not quantified results (spearman method)
  • Non-parametric method on more than two samples (Friedman´s test)

Day 5

  • Non-parametric regression methods (Theil´s test) quality control harts
  • Quality control charts
  • Shewhart and Cusum Chart
  • Experimental design and optimization methods
  • Factorial designs
  • Estimation of factors interaction by two-way ANOVA test
  • Optimization method and three factors design

Course Methodology

A variety of methodologies will be used during the course that includes:

  • (30%) Based on Case Studies
  • (30%) Techniques
  • (30%) Role Play
  • (10%) Concepts
  • Pre-test and Post-test
  • Variety of Learning Methods
  • Lectures
  • Case Studies and Self Questionaires
  • Group Work
  • Discussion
  • Presentation

Who should attend

This course is intended for all Laboratory Managers, Analytical Chemists, Medical Scientists, Laboratory Supervisors, Research and Development Scientists, Microbiologists, Food Technologists and Quality Assurance/Control Managers.

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