概括性
齿轮
计算机科学
实证研究
心理学
程序设计语言
认知科学
人工智能
数学
统计
心理治疗师
作者
Daniel J. Simons,Yuichi Shoda,D. Stephen Lindsay
标识
DOI:10.1177/1745691617708630
摘要
Psychological scientists draw inferences about populations based on samples-of people, situations, and stimuli-from those populations. Yet, few papers identify their target populations, and even fewer justify how or why the tested samples are representative of broader populations. A cumulative science depends on accurately characterizing the generality of findings, but current publishing standards do not require authors to constrain their inferences, leaving readers to assume the broadest possible generalizations. We propose that the discussion section of all primary research articles specify Constraints on Generality (i.e., a "COG" statement) that identify and justify target populations for the reported findings. Explicitly defining the target populations will help other researchers to sample from the same populations when conducting a direct replication, and it could encourage follow-up studies that test the boundary conditions of the original finding. Universal adoption of COG statements would change publishing incentives to favor a more cumulative science.
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