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Mastering statistics relevant to Evolutionary Anthropology is often a learn-as-you-go process that takes plenty of time, practice, and discussion.  Remember that there is more to the topic of "statistics" than simply identifying which results are "significant."  In addition to deciding which results are most meaningful and how they should be interpreted, you also need to be able to represent your data and results in a visually compelling way, and to present your methods in a way that another scientist could understand and even replicate your findings.

Finding an appropriately-leveled statistics course is often a first important step.  Even with a course, however, you will likely find that you need to catch up on particular concepts or expand into specific methods that are not covered in a course.  Here are some suggestions for other resources to check out to better establish your foundations or to find instruction on specific skills:

  • For general concepts, check out these Mod U videos on causal inference from the Social Science Research Institute at Duke. They are great for brushing up on concepts or expanding your understanding.  
  • Also, Duke’s Center for Data and Visualization Sciences is an excellent resource for students, faculty, and staff alike. You can set up an appointment with one of their consultants, especially if you need some tips for navigating through R.
  • Many in the Nunn lab use Bayesian statistical methods.  For a great introduction - which covers core statistical concepts in an innovative way - check out Richard McElreath's "Statistical Rethinking" book, videos, and R-package.  It is challenging, but also feasible to work through.
  • Through your affiliation with Duke, you also have access to Coursera online courses, such as those in their Statistics Specialization.
  • Most people in the lab are using R.  This book is a great introduction to R and the tidyverse collection of R packages for data science.

If you have more specific questions about statistical methods, be sure to spend time working in the Nunn lab, as others who are in the lab would be happy to take a look at your project and answer questions. Alternatively, you can always set up a meeting with Charlie or another Nunn lab member to have a conversation about methods.

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