Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck's recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck's method are discussed and compared with solutions provided in high precision libraries.

经典连续拟合优度（GOF）测试用于检查数据是否来自假定的参数模型。在许多情况下，GOF测试假定均值分布为零，并检查样本阶跃统计的极值。这些统计中的许多可以通过阶次统计的函数来表示，并且p值等于基于统一阶次统计的联合概率陈述。在本文中，我们利用Steck的递归方法，提出了两种高精度计算算法来计算这些GOF统计数据的p值。讨论了实现Steck方法的数值困难，并将其与高精度库中提供的解决方案进行了比较。