Desired and Feared-What Do We Do Now and Over the Next 50 Years?
Desired and Feared-What Do We Do Now and Over the Next 50 Years?
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DOI：
10.1198/tast.2009.09045
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发表时间：
2009-08-01
影响因子：
1.8
通讯作者：
Meng, Xiao-Li
中科院分区：
文献类型：
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作者：
Meng, Xiao-Li
一场关于哈佛大学通识教育课程的激烈辩论表明，统计学作为一门学科，现在既令人渴望，又令人恐惧。这一新地位带来了一系列巨大的挑战。我们不再只是享受在每个人的后院玩耍或打扫后院的特权。我们现在被邀请进入每个人的书房或客厅，并被委托成为他们后代的第一个量化保姆。考虑到我们的职业规模相对于我们的宿主及其后代的绝对数量来说微不足道，我们能胜任这样一项令人伤脑筋的任务吗？布朗和卡斯的《什么是统计学？“（2009年），本文进一步建议如何使我们的专业在数量和质量上满足不断增长的需求。讨论了：（1）需要用专业发展课程（PDC）来补充我们的研究生课程;（2）需要在本科阶段开发更多的学科导向统计（SOS）课程和快乐课程;（3）需要有最合格的统计学家（无论是教学还是研究证书）来教授统计学入门课程，特别是其他学科的课程;（4）在教学和研究中，既要深化基础，又要拓展视野;（5）通过实践和教学，大力提高对无原则的数据分析方法的认识和避免，以此来对抗“激励性偏差”，这是造成科学错误发现、媒体误导信息和社会误导政策的罪魁祸首。
An intense debate about Harvard University's General Education Curriculum demonstrates that statistics, as a discipline, is now both desired and feared. With this new status comes a set of enormous challenges. We no longer simply enjoy the privilege of playing in or cleaning up everyone's backyard. We are now being invited into everyone's study or living room, and trusted with the task of being their offspring's first quantitative nanny. Are we up to such a nerve-wracking task, given the insignificant size of our profession relative to the sheer number of our hosts and their progeny? Echoing Brown and Kass's "What Is Statistics?" (2009), this article further suggests ways to prepare our profession to meet the ever-increasing demand, in terms of both quantity and quality. Discussed are (1) the need to supplement our graduate curricula with a professional development curriculum (PDC); (2) the need to develop more subject oriented statistics (SOS) courses and happy courses at the undergraduate level; (3) the need to have the most qualified statisticians-in terms of both teaching and research credentials-to teach introductory statistical courses, especially those for other disciplines; (4) the need to deepen our foundation while expanding our horizon in both teaching and research; and (5) the need to greatly increase the general awareness and avoidance of unprincipled data analysis methods, through our practice and teaching, as a way to combat "incentive bias," a main culprit of false discoveries in science, misleading information in media, and misguided policies in society.