Design and analysis of confirmation experiments

The statistical literature and practitioners have long advocated the use of confirmation experiments as the final stage of a sequence of designed experiments to verify that the optimal operating conditions identified as part of a response surface methodology strategy are attainable and able to achieve the value of the response desired. However, until recently there has been a gap between this recommendation and details about how to perform an analysis to quantitatively assess whether the confirmation runs are adequate. Similarly, there has been little in the way of specific recommendations for the number and nature of the confirmation runs that should be performed. In this article, we propose analysis methods to assess agreement between the mean response from previous experiments and the confirmation experiment, and suggest a strategy for the design of confirmation experiments that more fully explores the region around the optimum.

长期以来，统计学界文献与实务从业者均倡导将验证试验（confirmation experiment）作为一系列试验设计流程的最终阶段，用以验证通过响应面法（Response Surface Methodology）策略所确定的最优操作条件是否具备可实现性，且能否达成预期的响应目标值。然而直至近期，该倡导的实践方案与如何开展定量分析以评估验证试验是否充分的具体细节之间，仍存在显著的研究空白。类似地，针对应开展的验证试验的数量与试验类型，目前也鲜有明确的指导性建议。本文提出了若干分析方法，用于评估既往试验的平均响应与验证试验结果之间的一致性，并针对验证试验的设计提出了一套优化策略，可更充分地探索最优解周边的参数区域。