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Summary

Sample sizes for process validation can be determined using the success-run theorem, which uses the confidence level and reliability value to calculate statistically valid sample sizes.
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The FDA defines validation as a means established by objective evidence, and sample sizes should be determined based on the process's requirements.
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The first method does not allow or account for failures and is easier to calculate, while the second method requires the use of a chi-square distribution degree of freedom (df) calculation and table value look-up.
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Summary
This article demonstrates two methods using the success-run theorem, which uses the confidence level and reliability value to determine appropriate statistically valid sample sizes for process validation. The first method does not allow or account for failures and is easier to calculate, while the second method allows for a predetermined number of failures when measuring, testing, or evaluating the outputs of a validation. The first method does not allow or account for failures and is easier to calculate, while the second method requires the use of a chi-square distribution degree of freedom (df) calculation and table value look-up.

How To Establish Sample Sizes For Process Validation Using The Success-Run Theorem
bioprocessonline.com

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purdue.edu

Summary
Process validation is becoming increasingly important within regulated and non-regulated industries, and is comprised of three interrelated steps: Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ). Sample sizes are important to consider when performing process validations, and the FDA defines validation as a means established by objective evidence, a process that consistently produces a result, or product meeting its predetermined specifications. Sample sizes should be determined based on the process's requirements, and the number of samples needed to perform process validations should be determined by the manufacturer.

Sample Sizes: How Many Do I Need? | 2014-07-07 | Quality Magazine
qualitymag.com

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fda.report

Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".

Bayesian inference - Wikipedia
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