Binomial test

Name: Binomial test
Published: 2019-01-23
License: CC Attribution-ShareAlike 4.0 International

Stephen Gosnell

doi:10.25334/Q4WB30

Binomial test

Baruch College, City University of New York & PhD Program in Biology, The Graduate Center of the City University of New York

This lesson focused on introducing students to binomial data analysis and reinforcing concepts of confidence intervals, p-value interpretation, and one- vs two-sided tests.

Listed in Teaching Materials | resource by group Reducing Barriers to Teaching with R in Undergraduate Biology

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Version 1.0 - published on 23 Jan 2019 doi:10.25334/Q4WB30 - cite this

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

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Description

Following the lesson, students should be able to
   •   Download and open swirl package
   •   Input new course material into swirl
   •   Conduct out one- and two-sided binomial tests
   •   Interpret p-values
   •   Construct and interpret Agresti-Coull confidence intervals for binomial data

This swirl resource was used following an introduction to binomial tests (slides @ https://docs.google.com/presentation/d/1o0_wbcZ0W_PAeGVmFF_C89jWdJU2htoMoPcVmIV8JPU/present#slide=id.g426b4e3a32_0_0).

I then discussed swirl with students (introductory statistics class who had been been using R for approximately 3 weeks) and had them download and install the package and course (instructions @ https://sites.google.com/view/biostats/lectures/hypothesis-testing-with-the-binomial-distribution). After the swirl lesson students completed an assignment focused on the binomial distribution (https://docs.google.com/document/d/15Dk906z8L_pW9ySj9hIpRfwUdtI0L37Yx1IA7cmxZtg/edit
). Note the full lesson (slides, assignments) also included a Bayesian perspective and introduction to ggplot2 that were not part of the swirl course. These can be removed if focus is solely on traditional binomial analysis.

Swirl lesson required ~ 25 minutes. Pre-swirl lecture required ~1.5 hr and post-swirl worksheet required ~ 1 hr. Removing extra (Bayesian and ggplot2) material should reduce lecture time to ~ 1 hr and worksheet time to 25 minutes.