一个简单的,统计学上可靠的歧视测试.
Johann D Gaebler1, Sharad Goel2
1Department of Statistics, Harvard University, Cambridge, MA 02138.
概括
这项研究引入了一种新的统计方法来检测歧视. 当两个常见的测试一致时,它们可靠地识别偏见,揭示了加利福尼亚州警察停车场普遍存在的种族歧视.
科学领域:
- 社会科学 社会科学 社会科学
- 统计 统计 统计 统计
- 犯罪学 犯罪学
背景情况:
- 对歧视的观察性研究通常使用基准或结果测试.
- 这些个别测试具有统计上的局限性,可能会错误地暗示歧视.
研究的目的:
- 开发一种统计学上可靠的方法来检测歧视.
- 解决现有的基准和结果测试的局限性.
主要方法:
- 证明统计保证:在非参数假设下,至少两个常见的歧视测试中的一个必须是正确的.
- 开发一种混合测试,其中基准测试和结果测试之间的协议保证了正确的结论.
- 在贷款,教育和刑事司法领域实证验证这一假设.
- 评估混合测试对违反假设的稳定性.
主要成果:
- 混合测试提供了强有力的统计保证,可以在基准测试和结果测试一致时检测歧视.
- 混合测试的基本假设大约在关键的社会领域保持.
- 混合测试是强大的,以适度违反假设.
- 分析了280万次加利福尼亚警察停车,发现了广泛存在种族歧视的证据.
结论:
- 新的混合统计方法可靠地检测歧视.
- 调查结果提供了强有力的证据,证明加利福尼亚州的警察存在种族歧视.
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