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Tolerance interval testing for assessing accuracy and precision simultaneously.

Chieh Chiang1, Chin-Fu Hsiao1

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|February 5, 2021
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Summary
This summary is machine-generated.

This study introduces a comprehensive statistical inference framework for tolerance interval testing, crucial for analytical procedure validation. The new method enables accurate sample size determination, power analysis, and p-value calculation for enhanced accuracy and precision assessments.

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Area of Science:

  • Analytical Chemistry
  • Statistical Inference
  • Method Validation

Background:

  • Tolerance intervals are recommended for validating analytical procedure accuracy and precision.
  • Statistical inference methods for tolerance interval hypothesis testing are limited.
  • A robust statistical framework is needed for reliable method validation.

Purpose of the Study:

  • To establish a complete statistical inference for tolerance interval testing.
  • To develop methods for sample size determination, power analysis, and p-value calculation.
  • To provide a statistically sound approach for validating analytical procedures.

Main Methods:

  • Treating tolerance interval bounds as random variables.
  • Deriving a bivariate distribution for statistical inference.
  • Utilizing simulations to confirm theoretical properties.
  • Illustrating the method with a practical example.

Main Results:

  • A comprehensive statistical inference framework for tolerance interval testing was established.
  • The proposed method allows for accurate sample size determination and power analysis.
  • P-value calculation is integrated into the hypothesis testing framework.
  • Simulations validated the theoretical properties of the developed method.

Conclusions:

  • The developed method provides a complete statistical inference for tolerance interval testing.
  • This approach enhances the validation of analytical procedure accuracy and precision.
  • The framework offers a statistically rigorous tool for quality control in analytical chemistry.