Related Experiment Video
Updated: Jan 10, 2026

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
Published on: August 17, 2015
Confidence Intervals for Validation of Analytical Procedures Under ICH Q2(R2)
Paul Faya1, Chad N Wolfe1, Adam P Rauk1
1Eli Lilly and Company, Discovery and Development Statistics, Indianapolis, Indiana, USA.
Abstract:
The International Conference on Harmonisation (ICH) adopted revision 2 (R2) of its Quality Guidance Q2 Validation of Analytical Procedures in 2023. The revision includes a new statement that confidence interval limits for validation performance characteristics (i.e., accuracy and precision) should be compatible with acceptance criteria, unless otherwise justified. It also allows for sponsors to use prior knowledge (e.g., from development or previous studies) to support the validation study conclusion. These two new aspects of ICH Q2(R2) present both opportunities and challenges for the validation of analytical procedures. In this paper, we provide comprehensive examples of how to compute confidence intervals for key validation performance characteristics under different modeling approaches. We also describe methodologies for combining prior knowledge with the validation study results, both from frequentist and Bayesian perspectives. The paper is written in tutorial style and is aimed at statisticians and analytical scientists responsible for validating analytical procedures in compliance with ICH Q2(R2).
More Related Videos
07:57Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
Published on: July 25, 2014
10:22Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
Published on: September 7, 2019
Related Concept Videos
Data Validation
Key parameters for method validation include:
Finding Critical Values for Chi-Square
Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Uncertainty: Confidence Intervals