- Prepare (due M 2/7)
- Content below
- Sakai quizzes

- Peer Instructions – See on the class forum
- Homework (due Su 2/13)
- Worked Example

## Content

5.A – Confidence Intervals and Bootstrapping

- Intro Confidence Intervals (17 min.)
- Confidence Intervals in Python (17 min.)

5.B – Hypothesis Testing

- Intro Hypothesis Testing and Proportions (14 min.)
- Hypothesis Testing Means and More (33 min.)

## Optional Supplements

You can access an excellent free online textbook on OpenIntro Statistics here, co-authored by Duke faculty. You can pay a suggested but adjustable price for a tablet-friendly pdf, but you can also just get the regular pdf for free. For Module 3B, the following optional readings may be particularly helpful supplements:

- Chapter 5.2 Confidence intervals for a proportion. This provides introductory material on confidence intervals elaborating on 3B.A.1.
- Chapter 5.3 Hypothesis testing for a proportion. This elaborates on the introduction to hypothesis testing from 3B.B.1.
- Chapters 7.1, 7.3, and 7.5 cover material from 3B.B.2 on using t-tests for a single mean, the difference of two means, and many pairwise means respectively.
- Chapter 6.3 discusses the chi-square test for categorical data introduced in 3B.B.2.

In addition, here is the documentation for the scipy.stats library that implements most of the functionality described here as well as many other useful statistical functions.