文章基本信息

标题：Issues with data and analyses: Errors, underlying themes, and potential solutions
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作者：Andrew W. Brown ; Kathryn A. Kaiser ; David B. Allison 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2018
卷号：115
期号：11
页码：2563-2570
DOI：10.1073/pnas.1708279115
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Some aspects of science, taken at the broadest level, are universal in empirical research. These include collecting, analyzing, and reporting data. In each of these aspects, errors can and do occur. In this work, we first discuss the importance of focusing on statistical and data errors to continually improve the practice of science. We then describe underlying themes of the types of errors and postulate contributing factors. To do so, we describe a case series of relatively severe data and statistical errors coupled with surveys of some types of errors to better characterize the magnitude, frequency, and trends. Having examined these errors, we then discuss the consequences of specific errors or classes of errors. Finally, given the extracted themes, we discuss methodological, cultural, and system-level approaches to reducing the frequency of commonly observed errors. These approaches will plausibly contribute to the self-critical, self-correcting, ever-evolving practice of science, and ultimately to furthering knowledge.
关键词：rigor ; statistical errors ; quality control ; reproducibility ; data analysis