Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
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DOI：
10.1017/9781107286184
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发表时间：
2018-01-01
期刊：
影响因子：
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通讯作者：
Mayo, D. G.
中科院分区：
文献类型：
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作者：
Mayo, D. G.
社会科学和生物科学的复制失败越来越多，这使得批判性地评估拟议的改革成为一种新的紧迫性。这本书揭开了负责恢复科学诚信的专家之间的分歧。它否定了概率在推理中的作用的两种普遍观点：分配置信度，以及长期控制错误率。如果统计消费者不知道竞争对手证据改革背后的假设，他们就无法仔细检查影响他们的后果（在个性化医疗，心理学等方面）。这本书用一个简单的工具启航：如果在推断一个主张时几乎没有做什么来排除缺陷，那么它就没有通过严格的测试。数据专家倡导的许多方法经不起严格的审查，并且与阻止或解释樱桃采摘和选择性报告的成功策略存在矛盾。通过一系列的游览和展览，归纳推理的哲学和历史变得生动起来。哲学工具被用来解决科学与伪科学、归纳与证伪等问题。
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.