Statistical Bootstrapping with the R infer Package

Understanding the reliability of your data estimates is a core requirement for modern data analysis, yet traditional formula-based statistics can feel abstract and confusing. By shifting to a simulation-based approach, you can intuitively grasp how sampling variability works without memorizing complex equations. This course guides you through statistical inference using the tidyverse-friendly infer package in R, focusing on the powerful percentile method. You will start by mastering foundational statistical concepts, learning how bootstrapping mimics sampling from a population by resampling your original dataset. Next, you will write clean, readable R code to generate bootstrap distributions and calculate robust confidence intervals. Finally, you will learn to interpret these intervals correctly to make data-driven decisions with confidence. What you'll learn: - Understand the core principles of bootstrapping and simulation-based statistical inference - Generate bootstrap distributions using the clean, expressive syntax of the infer package - Calculate confidence intervals using the percentile method for various sample statistics - Apply modern tidyverse workflows to clean, manipulate, and visualize your resampling results - Interpret confidence intervals accurately to avoid common statistical pitfalls This text-based course is designed for beginners who have a basic familiarity with R syntax and want to build a practical, modern foundation in statistical computing. No advanced mathematics or prior statistics background is required. Start learning today to unlock the power of simulation-based statistical analysis in R.