Statistics as a Science, Not an Art: The Way to Survive in Data Science

Mark van der Laan

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Statistics as a Science, Not an Art: The Way to Survive in Data Science

机译：统计学是一门科学，而不是一门艺术：数据科学中的生存之道

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My father told me the most important thing about solving a problem is to formulate it accurately, and one would think most of us statisticians would agree. Suppose we want to build a spaceship that can fly to Mars and return safely to Earth. It would be tragic folly to make simplifying assumptions that are known to be false, since that would mean the death of the astronauts and certainly the failure of their mission. However, it is not true that one must start with simplifying assumptions, as the field of statistics has the theoretical foundation that allows the precise formulation of the statistical estimation problem. The foundation of statistics laid down by its founders, which is incorporating knowledge about the data-generating experiment through the statistical model and the formal definition of the question of interest through the definition of the estimand, could not have been to arbitrarily select a "convenient" statistical model. However, that is precisely what most statisticians blithely do, proudly referring to the quote, "All models are wrong, but some are useful." Due to this, models that are so unrealistic that they are indexed by a finite dimensional parameter are still the status quo, even though everybody agrees they are known to be false.

机译：父亲告诉我，解决问题最重要的事情是准确地制定问题，而且人们会认为我们大多数统计学家都会同意。假设我们要建造一个可以飞向火星并安全返回地球的太空飞船。简化已知错误的假设将是可悲的愚蠢，因为这将意味着宇航员的死亡以及任务的失败。但是，由于统计领域具有理论基础，可以精确地提出统计估计问题，因此必须从简化假设开始是不正确的。它的创始人奠定了统计学的基础，即通过统计模型结合有关数据生成实验的知识，并通过估计的定义来整合感兴趣的问题的正式定义，这不可能是随意选择“方便”统计模型。但是，这恰恰是大多数统计学家乐于做的，骄傲地提到这句话：“所有模型都是错误的，但有些模型是有用的。”因此，即使每个人都认为已知的模型是错误的，但如此不切实际的模型仍然只能通过有限的尺寸参数进行索引。

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《AMSTAT news》 |2015年第452期|29-30|共2页
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Mark van der Laan;
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1. In Response to 'Statistics as a Science, Not an Art: The Way to Survive in Data Science' by Mark van der Laan [J] . AMSTAT news . 2015,第455期

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Statistics as a Science, Not an Art: The Way to Survive in Data Science

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