This course covers critical concepts such as significance, hypothesis testing, p-values, and effect sizes. Here we establish a solid foundation for future statistical concepts! This course provides a practical understanding of statistics, meant to have you interpreting simple results as quickly as possible.

This course focuses entirely around the data we use for our statistical analyses. We cover common data distributions, assumptions for statistical tests, and data transformations.

This course covers several common statistical methods, such as T tests, ANOVAs, Linear Regressions, and Contingency tables using the R programming language. We focus on understanding what each test is meant to do and how we can interpret the outputs from R.