This is a series of labs/tutorials currently under development (2020-2021) for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.

The first set of 13 labs roughly tracks “Thinking with Data” (Vokey & Allen, 2018) and “Answering questions with data” (Crump et al., 2018); the second set of labs (to be written on a weekly basis during the Spring 2021 semester) will roughly track “Experimental Design and Analysis for Psychology” (Abdi et al., 2009).

Although the primary aim is to create lab exercises that reinforce stats concepts and also train basic R coding skills for data-analysis, there are many side goals, including showing students the advantages of using R markdown and Github for creating and communicating research products. For example, aside from these tutorials, I have been developing an R package called vertical (Vuorre & Crump, 2020), that highlights the advantages of learning R for researchers in psychology. And, where possible, I hope to inject some of this broader discussion about awesome R tools and how to use them into the labs (at the same time, a deep-dive requires a separate course…maybe coming soon to a browser near you).