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Avoiding the Pain of Out-of-Specification Results

ABSTRACT

The pharmaceutical industry does not have a good track record of applying sound statistical principles to the investigation of out-of-specification (OOS) results. Recently, Steven Kuwahara presented an article on the history of the OOS problem highlighting some statistical deficiencies.1 In this article, we present some additional statistical principles. Failure to apply these principles causes unnecessary investigations, nearly guarantees recurrence, and wastes valuable time and resources. Control charts can help scientists and managers avoid these pitfalls.

In many factories and laboratories, the prevalent mindset is that if the test result under examination remains within ...

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