Empirical results hinge on analytical decisions that are defensible, arbitrary and motivated. These decisions probably introduce bias (towards the narrative put forward by the authors), and they certainly involve variability not reflected by standard errors. To address this source of noise and bias, we introduce specification curve analysis, which consists of three steps: (1) identifying the set of theoretically justified, statistically valid and non-redundant specifications; (2) displaying the results graphically, allowing readers to identify consequential specifications decisions; and (3) conducting joint inference across all specifications. We illustrate the use of this technique by applying it to three findings from two different papers, one investigating discrimination based on distinctively Black names, the other investigating the effect of assigning female versus male names to hurricanes. Specification curve analysis reveals that one finding is robust, one is weak and one is not robust at all.

经验结果取决于可辩驳，任意和有动机的分析决策。这些决定可能会引入偏见（偏向作者提出的叙述），并且肯定涉及标准误差未反映的可变性。为了解决这种噪声和偏差的来源，我们介绍了规范曲线分析，它包括三个步骤：（1）确定一组理论上合理的，统计上有效的和非冗余的规范；（2）以图形方式显示结果，使读者可以识别相应的规格决策；（3）跨所有规范进行联合推理。我们通过将这项技术应用于两篇不同论文的三项发现来说明这种技术的使用，其中一项研究基于明显的黑人名字的歧视，另一类调查将女性姓名与男性姓名分配给飓风的影响。规格曲线分析显示，一项发现是可靠的，一个是较弱的，而一个根本不可靠。