Seek a Paradigm and Distrust It? Statistical Arguments and the Representation of Uncertainty
Seek a Paradigm and Distrust It? Statistical Arguments and the Representation of Uncertainty
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寻找一个范式并且不信任它？
DOI：
10.1162/99608f92.a02188d0
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Pescott O
中科院分区：
文献类型：
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作者：
Pescott O
Bailey（2023a）强调了Meng（2018）方程为该领域当前统计实践提供的见解，为民意调查和更广泛的调查抽样社区提供了非常有用的服务。通过强调数据缺陷相关性（ρ）和总体规模对误差的独立和交互影响的连续性（抛开“问题难度”），我们清楚地看到，仅仅依赖期望（在统计和日常意义上）会如何让我们在现实世界的问题中误入歧途。虽然我完全赞同 Bailey (2023a) 的总体信息，即孟恒等式的清晰性迫使分析师更加认真地对待随机（或任何概率）抽样的偏离，并为理解现有加权类型方法的逻辑统一提供了一个简洁的框架（正如孟，2022a 优雅地解释的那样），但我对他提出的范式转变有两个小评论。第一个与随机缺失（MAR）或条件忽略工具箱中的现有方法看似相当强烈的驳回有关；第二个在此基础上考虑更广泛的问题，即与非概率样本的描述性推论相关的潜在不确定性的充分沟通。
Bailey (2023a) does a very useful service to the polling and broader survey sampling communities by highlighting the insights that the Meng (2018) equation provides into current statistical practice in that area. Through the emphasis on the continuous nature of the independent and interactive effects of the data defect correlation (ρ) and population size on error (putting aside the ‘problem difficulty’), we see clearly how relying solely on expectations (in both the statistical and everyday senses) can lead us astray in real-world problems. While I fully endorse the general message of Bailey (2023a) that the clarity of the Meng identity forces analysts to take departures from random (or any probability) sampling more seriously, and provides a neat framework for understanding the logical unity of existing weighting-type methods (as elegantly explained by Meng, 2022a), I have two small comments regarding his proposed paradigm shift. The first relates to the seemingly quite strong dismissal of the existing methods within the missing at random (MAR) or conditional ignorability toolbox; the second builds on this to consider the wider problem of the full communication of the potential uncertainty associated with descriptive inferences from nonprobability samples.