This is a series of labs/tutorials 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”1 and “Answering questions with data”;2 the second set of labs roughly tracks “Experimental Design and Analysis for Psychology.”3

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,4 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).