Statistics and Study Design in Ecology and Conservation

Curriculum

An understanding of statistics and study design is essential to success in the fields of ecology and conservation. However, many of the analyses of greatest utility for ecological data are not addressed in introductory courses, while advanced courses often delve deeply into a limited set of techniques. This course, now available to professionals, helps to bridge this gap: by covering both traditional core statistical techniques as well as newer advancements such as mixed effects models, all focusing on common data types in these disciplines. Participants will develop skills in data manipulation and analyses using the R statistical computing environment, a popular and powerful tool commonly used in ecology.

This course addresses the fundamentals of study design, linking choices made when establishing a research project to the types of analyses appropriate to the chosen design, including those common to ecological studies such as repeated measures and blocked designs. The course focuses on univariate tests and models, or those with only categorical predictors (e.g. ANOVA), as well as non-parametric alternatives to these approaches. This course builds a strong and critical background for those looking to move to more advanced statistical approaches, such as those covered in our Generalized Linear and Mixed Models in Ecology and Conservation Biology course. Each of the five three-week modules includes a set of instructional videos and associated exercises, as well as a problem set. An introduction to coding in R is included in this course: no prior experience in R is expected or required.