Statistical models have seen a significant rise in popularity in recent years. Despite their undeniable success in various industry use cases such as sabermetrics, investment portfolio management, and artificial intelligence, there has been immense debate about the value of results produced by statistical methods. This paper focuses on presenting the common issues practitioners have when implementing statistical learning models, and why these issues make it difficult to interpret results produced by such methods.

翻译：近年来，统计模型的应用日益普及。尽管在赛伯计量学、投资组合管理和人工智能等众多工业场景中取得了显著成功，但关于统计方法所产生结果的价值一直存在大量争议。本文聚焦于实践者在实施统计学习模型时遇到的常见问题，并阐释为何这些问题导致此类方法产生的结果难以解读。