We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.

翻译：我们研究如何对观测和预测的样本进行测试,以评估预测是否谨慎。谨慎要求观测-预测对的区别的平均值可以显示为明显负值。关于安全的结论,我们建议测试未加权(或同等加权)和加权手段,并明确考虑到个别对的随机性。提出的测试方法主要指明为靴套和正常近似算法。这些测试是一般性的,但可特别用于信用风险领域,包括用于监管和会计目的。