Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection

Danielle J. Navarro

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Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection

机译：魔鬼和深蓝色大海:之间的紧张局势在科学判断和统计模型选择

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Discussions of model selection in the psychological literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data.Within this setting, advocates of leaveone- out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer. In this comment, I discuss some of these issues from a scientific perspective. What goal does model selection serve when all models are known to be systematically wrong? How might "toy problems" tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not offer answers to these questions, but hope to highlight the reasons why psychological researchers cannot avoid asking them.

机译：讨论的模型选择心理学文献通常帧问题统计推断的问题,我们的目标是确定哪些模型让最好的预测数据。设置,提倡leaveone -交叉验证和贝叶斯因子上存在分歧精确的预测模型问题选择应该回答的问题。评论,我从一个讨论其中的一些问题科学的视角。选择服务当所有模型是已知的系统错了吗?告诉一个误导的故事吗?解释结合的目标(或不同)传统的统计问题吗?这些问题的答案,但是希望突出心理研究者不能的原因避免让他们。

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来源
《Computational brain & behavior》 |2019年第1期|28-34|共7页
作者
Danielle J. Navarro;
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作者单位

School of Psychology, University of New South Wales, Kensington, Sydney, NSW 2052, Australia;

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原文格式 PDF
正文语种英语
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关键词
model selection; statistical models (nuclear); ScienceJudgmentbackground programDeep BlueStatistical inference;

机译：模型选择;统计模型(核);ScienceJudgmentbackground programDeepBlueStatistical推理;

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Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection

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