Choosing the Best Device Sample Size
for Verification and Validation
If you're like many manufacturers, you understand the essence of the 21 CFR 820.30 requirement: you must run enough test samples of a product so its test results can be successfully applied to full-scale production runs. (Also, your sample sizes must be appropriate for the type of testing you're doing and the type of product.)
And, like many manufacturers, you've probably had trouble for years determining exactly how many units of a product you should test to satisfy the FDA.
Choosing the Best Device Sample Size for Verification and Validation will help you select the right statistical methods to make this determination.
With it, you'll learn how to get the right sample size to ensure that user requirements are met in the product design.
Through the use of anecdotal examples that clearly illustrate complex concepts and a collection of statistical formulae this management report will help you understand how to:
Examine the discrete or continuous statistical data you collect. When testing involving discrete data, you'll be doing simple pass/fail tests. With continuous data, you'll measure the output of a device, such as cycle times, voltages or pressures.
Determine how many units you must test to provide sufficient confidence that zero failures in the sample can be interpreted to mean that the product meets the user requirements, including safety factors.
Look at variability, including variation from unit to unit or from batch to batch, as well as variation in their measurement systems.
Design verification and validation tests, particularly regarding choice of sample size.
Fully understand the requirements for statistical techniques, including how different techniques can affect the design control process.
Determine what is, and what is not, statistical in nature
And much, much more.
Finally, you can gain a clearer understanding of how to put together a statistical methods program for design verification and validation that will satisfy FDA auditors.
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Table of Contents
Statistical Techniques and Design Controls
Statistical Requirements for Verification and Validation
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