PINCONGESSENCE Telegram 2547
我第一次读到这篇文章的时候就想在频道分享,只是当时对直接转发英文文章这件事还不太确定。最近陆续收到一些读者的反馈,看到大家喜欢频道分享的英文资料,我觉得很开心。

今天分享这篇文章的主题是,一个受过教育的公民到底应该具备怎样的统计学和概率知识。作者在文中总结了七个常见误区,并且通过具体实例来说明为什么这些知识和我们的生活息息相关。

读完后会发现,作者喊话的对象是那些负责在课堂教授基础统计课程的教师们,而喊话的目的在于推动统计教育的变革。尽管如此,我认为这篇文章写得非常好,适合推荐给所有渴望提升自己的读者。

这七个容易引起误解的统计学话题分别是:
1. Cause and Effect
2. Statistical Significance & Practical Importance
3. Low Power vs. No Effect
4. Biases in Survey
5. Probable Coincidences
6. Confusion of The Inverse ( Conditional Probability)
7. Average vs. Normal

阅读这篇文章并不需要读者具备过硬的统计学背景,只需要一点耐心和一段可以用来思考的空闲时间。如果你认为作者的观点有道理,并且想要进一步自学统计方面的知识,那我再推荐一本很耐读的统计学经典教材:

Statistics by David A. Freedman

#教育 #统计 #公民常识



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我第一次读到这篇文章的时候就想在频道分享,只是当时对直接转发英文文章这件事还不太确定。最近陆续收到一些读者的反馈,看到大家喜欢频道分享的英文资料,我觉得很开心。

今天分享这篇文章的主题是,一个受过教育的公民到底应该具备怎样的统计学和概率知识。作者在文中总结了七个常见误区,并且通过具体实例来说明为什么这些知识和我们的生活息息相关。

读完后会发现,作者喊话的对象是那些负责在课堂教授基础统计课程的教师们,而喊话的目的在于推动统计教育的变革。尽管如此,我认为这篇文章写得非常好,适合推荐给所有渴望提升自己的读者。

这七个容易引起误解的统计学话题分别是:
1. Cause and Effect
2. Statistical Significance & Practical Importance
3. Low Power vs. No Effect
4. Biases in Survey
5. Probable Coincidences
6. Confusion of The Inverse ( Conditional Probability)
7. Average vs. Normal

阅读这篇文章并不需要读者具备过硬的统计学背景,只需要一点耐心和一段可以用来思考的空闲时间。如果你认为作者的观点有道理,并且想要进一步自学统计方面的知识,那我再推荐一本很耐读的统计学经典教材:

Statistics by David A. Freedman

#教育 #统计 #公民常识

BY 蛋挞报


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