1. Prepare (due Mon 10/16)
    1. Content below
    2. Canvas quizzes
  2. Peer Instructions – See on the class forum
  3. Homework (due Sun 10/22) [Link]
  4. Worked Example [Link]


Note: the slides for this module have been updated. Please switch to the “slides” panel when viewing the video in Panopto. DO NOT stay on the “screen” panel, as the recorded screen showed the old slides (which contained typoes and old information).

07.A – Confidence Intervals and Bootstrapping

  1. Intro Confidence Intervals (17 min.)
  2. Confidence Intervals in Python (17 min.)
  3. Misconceptions about Confidence Intervals (short read)
    The 3rd paragraph (starting with “As a technical note…” in this link

07.B – Hypothesis Testing

  1. Intro Hypothesis Testing and Proportions (14 min.)
  2. 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 7, 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 5.A.1.
  • Chapter 5.3 Hypothesis testing for a proportion. This elaborates on the introduction to hypothesis testing from 5.B.1.
  • Chapters 7.1, 7.3, and 7.5 cover material from 5.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 5.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.