Statistical Inference with R and the infer Package

Statistical inference can seem daunting, but mastering its principles is crucial for making data-driven decisions. This course demystifies the core concepts, providing a clear pathway to understanding how to draw reliable conclusions from data. By the end of this course, you will be able to confidently apply foundational statistical inference techniques, interpret results, and present your findings using modern R tools. What you'll learn: * Understand the fundamental principles of statistical inference, including sampling distributions and hypothesis testing. * Apply the infer package in R to conduct various hypothesis tests and construct confidence intervals. * Interpret p-values, confidence intervals, and effect sizes in the context of real-world research questions. * Practice data manipulation and visualization using tidyverse tools to prepare data effectively for inferential analysis. * Learn to present statistical findings clearly and reproducibly using R Markdown for professional reports. * Differentiate between common inferential tests and select the appropriate method for diverse data types. This course begins with a thorough explanation of essential statistical concepts before progressing to hands-on application with the infer package. You will learn to set up analyses, execute tests, and interpret their outcomes, culminating in guidance on how to communicate your findings. This course is designed for beginners with no prior experience in statistical inference or the infer package. No prerequisites are required. Start your journey into robust statistical inference today.