Sequential (or adaptive) designs are common in acceptance sampling and pharmaceutical trials. This is because they can achieve the same type 1 and type 2 error rate with fewer subjects on average than fixed sample trials. After the trial is completed and the test result decided, we need full inference on the main parameter $\Delta$. In this paper, we are interested in exact one-sided lower and upper limits.

Unlike standard trials, for sequential trials there need not be an explicit test statistic, nor even $p$-value. This motivates the more general approach of defining an ordering on the sample space and using the construction of Buehler (1957). This is guaranteed to produce exact limits, however, there is no guarantee that the limits will agree with the test. For instance, we might reject $\Delta \le {\Delta }_0$ at level $\alpha$ but have a lower $1-\alpha$ limit being less then ${\Delta }_0$. This paper gives a very simple condition to ensure that this unfortunate feature does not occur. When the condition fails, the ordering is easily modified to ensure compatibility.

顺序（或自适应）设计在验收抽样和药物试验中很常见。这是因为与固定样本试验相比，它们平均可以用更少的受试者实现相同的 1 类和 2 类错误率。试验完成并确定试验结果后，我们需要对主要参数进行充分推断$\Delta$. 在本文中，我们对精确的单边下限和上限感兴趣。

与标准试验不同，对于连续试验，不需要明确的检验统计量，甚至 $磷$-值。这激发了在样本空间上定义排序并使用 Buehler (1957) 的构造的更一般方法。这可以保证产生准确的限制，但是，不能保证这些限制与测试一致。例如，我们可能会拒绝$\Delta \le {\Delta }_0$ 在水平 $\alpha$ 但有一个较低的 $1-\alpha$ 限制小于 ${\Delta }_0$. 本文给出了一个非常简单的条件，以确保不会发生这种不幸的特征。当条件失败时，可以轻松修改排序以确保兼容性。