When establishing a hypothesis testing procedure to ensure its credibility, the most significant step is unquestionably to select and/or compute the optimal type-I and type-II error probabilities, namely the producer’s and consumer’s risks, or α & β errors, respectively if the research hypothesis is set to be a good product vs bad. This article is fundamentally opposed to conventionally and judgmentally picking at best a subjective type-I error probability (α error) and it therefore outlines a game theoretic approach, i.e. that of von Neumann, to this historically century-old unresolved paradigm to justify optimal choices when relevant market-centric factors such as cost and utility are incorporated for input data. A game theory-based algorithmic methodology and several detailed numerical examples of practical nature with specific emphasis to company-specific acceptance sampling plans (including a simple hospital scenario) for quality control are studied. A side benefit of this method, in addition to improving the enterprise acceptance sampling plans, is to transform the traditional hypothesis testing procedure so as to make sound engineering decisions from a “subjective” to an “objective” stance, provided that the monetary cost and utility values as consequences to committing error and non-error combinations are available.

When establishing a hypothesis testing procedure to ensure its credibility, the most significant of all is unquestionably to select and/or compute the optimal Type-I and the Type-II error probabilities, namely Producer’s and Consumer’s Risks, α & β respectively. This article as opposed to the conventionally and judgmentally picking at best a subjective Type-I error probability, outlines a Game theoretic approach, i.e. that of von Neumann, to this historically unresolved paradigm to justify optimal choices for Type-I error probability (α) and Type-II error probability (β) when cost, utility and other market-centric factors are incorporated as input data. A game theory-based algorithmic methodology and several numerical examples of practical nature with specific emphasis to company-specific Acceptance Sampling plans for Quality Assurance are illustrated. A side benefit of this method in addition to improving the Acceptance Sampling plans is to transform the traditional Hypothesis Testing process in making sound engineering decisions from a “subjective” to “objective” stance, provided that the monetary cost and utility consequences of committing error and non-error combinations are available.