Introduction to Statistical Analysis of Laboratory Data

Who Should Attend

This course is designed as an introduction to the statistical principles of laboratory data analysis and quality control that form the basis for the design and analysis of laboratory investigations. The course curriculum will benefit R&D managers, analytical laboratory supervisors and staff, manufacturing and production professionals, scientists, technicians and others who wish to comprehend and interpret methods of data analysis relevant to laboratory experimentation.

This training will concentrate on the philosophy and understanding of the statistical principles required in conducting sound scientific investigations of laboratory processes and validation.  It will not simply present statistical formulae and the lectures are oriented toward professionals having minimal formal training in statistics or mathematics beyond basic algebra.  However, for those with more formal training in statistics wishing to actually apply the techniques, appropriate time and references will be given for the procedures involved.
 


Learning Objectives

Those completing the course will have an understanding of the concepts of statistical graphing methods required in laboratory data analysis and validation.  Attendees will be able to interpret results related to design and analysis issues as presented in scientific literature concerning laboratory data analysis, as well as, quality control methods.

Course Description

Basic Methods (Day One). This section of the course will detail the basic and intermediate statistical concepts that are essential for professionals in the field. The first day emphasizes the principles of descriptive and inferential statistical applications and focuses on actual study examples, problem solving and interpretation of results.  Throughout the course the participants are encouraged to ask questions and discuss examples relevant to their own work.  Topic areas to be discussed include, but are not limited to:

• Basic statistical terminology needed to effectively communicate and understand your data results
• The statistical testing  essentials required to initiate a research investigation (i.e., research questions in statistical terms)
• Concepts of accuracy and precision in measurement analysis to ensure appropriate conclusions in experimental results
• Discussion of statistical techniques to compare experimental approaches with respect to specificity, sensitivity and linearity

Advanced Topics (Day Two). This section of the course will go beyond the basics and cover more complex issues in laboratory investigations with examples.  Topics will include:

• Association studies including correlation and regression analysis with laboratory applications
• Analysis of robustness and ruggedness
• Method comparison using more accurate alternatives to correlate analysis and other pair-wise comparisons
• Outliers, limit of detection and limit of quantitation
• Statistical quality control for process stability and capability