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Conducting Analysis of Variance (ANOVA) in R with swirl

By Kevin Geyer

Young Harris College

This lesson offers an introduction to ANOVA, including 1) how this statistical test can be differentiated from others and 2) a step-by-step guide to conducting and interpreting ANOVA results in R, including assumption testing and post-hoc analysis.

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

Version 1.0 - published on 13 Jan 2020 doi:10.25334/92KV-EH29 - cite this

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


This lesson focuses on recognizing situations in which ANOVA can be used and the necessary steps for conducting such a test. Students will review the different types of variables commonly collected by biologists (continuous vs. categorical; dependent vs. independent) to discern scenarios in which ANOVA is appropriate. Next, students follow a step-by-step guide to conducting and interpreting an ANOVA test using the 'iris' dataset made available in base R. A variety of multiple choice and command-response questions are used to check student comprehension throughout. The skills learned within should serve as a foundation for additional tests utilizing other variable combinations and may require testing of other assumptions and/or data transformations.


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Teaching with R in Undergraduate Biology

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