Abstract

We propose a pathway for teaching and learning statistical inference using data science tools widely accepted in industry, academia, and government. We argue that our 'data science first' approach to statistical inference is both (1) feasible since the tidyverse is more intuitive for new R users to learn than base R and (2) worthwhile since our proposed pathway exposes students to data science tools applicable beyond the classroom. We first introduce the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. After equipping students with just enough of these data science tools to perform effective exploratory data analysis, we then guide students through traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, all while focusing on visualization and the use of real data throughout.