COURSE OVERVIEW:

The meaning of this course resides in the transformation of raw laboratory data into statistically significant evidence that supports scientific conclusions. For the laboratory chemist, biostatistics is the essential bridge between an observation and a validated result, providing the mathematical rigor needed to navigate the inherent variability of chemical measurements. This program moves beyond basic arithmetic to explore the deep logic of probability as it applies to the analytical bench.

The scope of the training is designed to address the specific statistical challenges faced in the laboratory environment, such as small sample sizes, detection limits, and non-normal distributions. It covers the application of hypothesis testing, regression analysis, and analysis of variance in the context of method validation and process control. Participants will learn to use statistical tools not just as a reporting requirement, but as a diagnostic aid to identify sources of error and improve method robustness.

The coverage includes comprehensive training on the calculation of measurement uncertainty, the detection of outliers, and the design of experiments (DoE). Attendees will explore the use of control charts for trend analysis and learn to interpret the statistical significance of inter-laboratory comparisons. By mastering these applications, chemists will be able to defend their data with confidence and provide the quantitative assurance necessary for regulatory and industrial compliance.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Apply the principles of normal and non-normal distributions to lab data.
• Perform hypothesis testing to determine the significance of analytical results.
• Utilize T-tests and F-tests to compare different methods and analysts.
• Execute Analysis of Variance (ANOVA) to identify sources of measurement bias.
• Construct and interpret linear and non-linear regression models for calibration.
• Calculate the Limit of Detection and Limit of Quantitation using statistical models.
• Implement Grubbs and Dixon tests to identify and manage data outliers.
• Design efficient laboratory experiments using Factorial Design principles.
• Calculate expanded measurement uncertainty budgets according to GUM guidelines.
• Create and monitor Shewhart and CUSUM control charts for quality control.
• Evaluate the results of proficiency testing and inter-laboratory studies.
• Utilize statistical software packages to automate complex data analysis.

TARGET AUDIENCE:

This course is intended for Analytical Chemists, Research Scientists, Quality Control Technicians, and Method Validation Specialists who require advanced skills in data interpretation and statistical modeling.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.