The post motivated by a tweetorial from Darren Dahly In an experiment, do we adjust for covariates that differ between treatment levels measured pre-experiment (“imbalance” in random assignment), where a difference is inferred from a t-test with p < 0.05? Or do we adjust for all covariates, regardless of differences pre-test? Or do we adjust only for covariates that have sustantial correlation with the outcome? Or do we not adjust at all?

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ggpubr is a fantastic resource for teaching applied biostats because it makes ggplot a bit easier for students. I’m not super familiar with all that ggpubr can do, but I’m not sure it includes a good “interaction plot” function. Maybe I’m wrong. But if I’m not, here is a simple function to create a gg_interaction plot. The gg_interaction function returns a ggplot of the modeled means and standard errors and not the raw means and standard errors computed from each group independently.

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R doodles. Some ecology. Some physiology. Much fake data.

Thoughts on R, statistical best practices, and teaching applied statistics to Biology majors

Jeff Walker, Professor of Biological Sciences

University of Southern Maine, Portland, Maine, United States