本章概览 (Guide)：

• 在前面几讲的学习中，我们研究的是总体或者总体中抽取的单独一个样本。而在实际的研究中，我们抽取的样本远远不止三个。在本章，我们将所有这些可抽取的样本构成一个集合，学习它们之间、它们与总体之间的关系。此外，我们还将初步了解假设检验的程序和两类错误。
  In the previous lectures, we studied a whole population or a single sample extracted from the population. In real research, we need to extract far more than three samples. In this chapter, we will form a set of all these extractable samples and learn their relationships with each other and with the population. In addition, we will also have a preliminary understanding of the hypothesis testing procedure and two types of errors.

学习要点 (Laerning Points)：

1. 掌握样本均值分布的特性（形状、均值、标准误）
   Master the characteristics of sample mean distribution (shape, mean, standard error)
2. 区分标准误、标准差和取样误差
   Distinguish standard error, standard deviation, and sampling error
3. 学会利用中心极限定律求解标准误
   Learn how to use the central limit law to calculate standard errors
4. 学会计算样本均值分布中的概率
   Learn how to calculate the probability in the sample mean distribution
5. 掌握假设检验的基本步骤
   Master the basic steps of hypothesis testing
6. 学会分辨假设检验的两类错误
   Learn how to distinguish two types of errors in hypothesis testing

目录

• 样本均值分布 (Sample Mean Distribution)
• 中心极限定律 (central limit law）
• 标准误与样本均值分布的概率 (standard error and probability of sample mean distribution)
• 假设检验的基本逻辑 (the basic logic of hypothesis testing)
• 假设检验的两类错误(two types of errors in hypothesis testing)

作业

• 2023作业 (homeworks of 2023)
• 2024作业 (homeworks of 2024)