The Seven Pillars of Statistical Wisdom
The Seven Pillars of Statistical Wisdom
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统计智慧的七大支柱
DOI：
10.1080/09332480.2018.1438714
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Robert
中科院分区：
文献类型：
--
作者：
C. Robert
即使是最基本的想法？聚合，以平均为例？是违反直觉的。它允许人们通过丢弃信息，即观察的个性来获得信息。Stigler的第二个支柱，信息测量，通过指出观测值并不都同样重要，挑战了“大数据”的重要性：数据集中的信息量通常只与观测值的平方根成正比，而不是与绝对数量成正比。第三个概念是可能性，即使用概率来校准推论。相互比较是指不需要对外部标准进行统计比较的原则。第五支柱是回归，这既是一个悖论（高的父母平均生出矮的孩子；高的孩子平均生出矮的父母），也是推理的基础，包括贝叶斯推理和因果推理。第六个概念抓住了实验设计的重要性。例如，通过认识严格随机化的组合方法所获得的收益。第七个概念是残余现象：一个复杂的现象可以通过减去已知原因的影响而简化，留下一个可以更容易解释的残余现象。
Even the most basic idea?aggregation, exemplified by averaging?is counterintuitive. It allows one to gain information by discarding information, namely, the individuality of the observations. Stigler’s second pillar, information measurement, challenges the importance of “big data” by noting that observations are not all equally important: the amount of information in a data set is often proportional to only the square root of the number of observations, not the absolute number. The third idea is likelihood, the calibration of inferences with the use of probability. Intercomparison is the principle that statistical comparisons do not need to be made with respect to an external standard. The fifth pillar is regression, both a paradox (tall parents on average produce shorter children; tall children on average have shorter parents) and the basis of inference, including Bayesian inference and causal reasoning. The sixth concept captures the importance of experimental design?for example, by recognizing the gains to be had from a combinatorial approach with rigorous randomization. The seventh idea is the residual: the notion that a complicated phenomenon can be simplified by subtracting the effect of known causes, leaving a residual phenomenon that can be explained more easily.