Connecticut's novel approach to collecting and analyzing traffic stop data for evidence of disparate treatment is widely considered to be a model of best practice. Here, we provide an overview of Connecticut's framework, detail solutions to the canonical empirical challenges of analyzing traffic stop, and describe a data‐driven approach to early intervention. Unlike most jurisdictions that simply produce an annual traffic stop report, Connecticut has developed an ongoing system for identifying and mitigating disparity. Connecticut's framework for identifying significant disparities on an annual basis relies on the so‐called “preponderance of evidence” approach. Drawing from the cutting‐edge of the empirical social science literature, this approach applies several, as opposed to a single, rigorous empirical test of disparity. For departments identified as having a disparity, Connecticut has developed a process for intervening on an annual basis. In that process, policing administrators engage with researchers to conduct an empirical exploration into possible contributing factors and enforcement policies. In Connecticut, this approach has transformed what had once been a war of anecdotes into a constructive data‐driven conversation about policy. Variants of the Connecticut Model have recently been adopted by the State of Rhode Island, Oregon, and California. Connecticut's approach provides a useful model and policy framework for states and localities conducting disparity studies of police traffic stops.

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测试交通站点差异：康涅狄格州模式的最佳做法

康涅狄格州采用新颖的方法收集和分析交通停车数据以证明有不同的待遇，这被认为是最佳做法的典范。在这里，我们提供了康涅狄格州框架的概述，详细介绍了分析交通停滞的典型经验挑战的解决方案，并描述了一种以数据驱动的早期干预方法。与大多数仅提供年度交通停止报告的司法管辖区不同，康涅狄格州已开发出一套持续不断的系统来识别和缓解差距。康涅狄格州每年识别重大差异的框架依赖于所谓的“优势证据”方法。从经验主义社会科学文献的前沿出发，这种方法适用于多种方法，而不是对差异进行单一，严格的经验检验。对于确定为存在差异的部门，康涅狄格州制定了一种年度干预程序。在此过程中，治安管理人员与研究人员合作，对可能的促成因素和执行政策进行实证研究。在康涅狄格州，这种方法已将曾经的轶事之争转变为关于政策的建设性数据驱动型对话。的变体康涅狄格州模型最近已被俄勒冈州罗得岛州和加利福尼亚州采用。康涅狄格州的方法为各州和地方进行警察交通站点差异研究提供了有用的模型和政策框架。