Randomness (in the sense of being generated in an IID fashion) and exchangeability are standard assumptions in nonparametric statistics and machine learning, and relations between them have been a popular topic of research. This short paper draws the reader's attention to the fact that, while for infinite sequences of observations the two assumptions are almost indistinguishable, the difference between them becomes very significant for finite sequences of a given length.

翻译：随机性（指以独立同分布方式生成）与可交换性是非参数统计学与机器学习中的标准假设，二者之间的关系一直是研究的热点课题。本文旨在提请读者注意：对于无限观测序列而言，这两种假设几乎无法区分；但对于给定长度的有限序列，二者之间的差异则变得极为显著。