USP 1010 Analytical Data – Interpretation and Treatment (On-Demand)

Curriculum

Course Description
This two-day course expands on the key elements of USP–NF General Chapter <1010> concerning acceptable statistical concepts and practices for the analysis and interpretation of analytical data. Special emphasis is placed on making statistical inference using statistical interval approaches, use of the right statistical methods to establish comparability, sample size calculation for significance and equivalent tests, outlier detection, and application of variance component analysis to tease out variations from different sources.

 

The live version of this recording took place on November 18-19, 2020.

This course contains English subtitles.

Upon completion of this course, you will be able to:

  • Define statistical concepts commonly encountered in the literature and the USP
  • Describe the measures of central tendency and creation of confidence intervals
  • Explain the use of hypothesis testing
  • Select the appropriate statistical tests
  • Interpret statistical outcomes

Who Should Participate:

  • Biologists
  • Analytical Scientists
  • Statisticians
  • Assay development scientists
  • Chemists
  • Quality engineers
  • QA/QC analysts
  • R&D scientists and managers
  • Regulatory affairs specialists
  • Site inspection and CMC review chemists
  • Manufacturing scientists and managers

Note: You should have at least two years’ experience in the pharmaceutical industry and an understanding of basic statistical concepts to get the full benefit of this course

Access Duration:

Access to this course expires 60 days from the date of registration or until you mark the course ‘Complete’ – whichever occurs first.

The accompanying USP General Chapter(s) available as resources with this course, were official as of the date indicated in the chapter PDF. Please check www.uspnf.com for relevant updates.

USP Approved Instructor

Steven Walfish
USP employee
M.S., statistics, Rutgers University
MBA, Boston University
B.A., statistics, University of Buffalo