Inferential Statistics and Sampling Methods with R

Understanding the story behind your data requires more than just looking at averages and charts. To make confident decisions, you need to understand how to draw accurate conclusions about entire populations from smaller samples using R. This text-based course guides you through the foundational principles of statistical inference and sampling methodologies, ensuring you can back up your business and research decisions with mathematical rigor. You will transition from a beginner to a confident data analyst capable of designing sound sampling strategies and executing robust statistical tests. By working through clear written explanations and practical R code snippets, you will learn how to avoid common analytical biases and interpret your findings accurately. What you'll learn: - Understand core statistical concepts including population parameters, sample statistics, and the Central Limit Theorem - Apply probability and non-probability sampling methods to collect representative data - Perform hypothesis testing, including t-tests, ANOVA, and chi-square tests using standard R functions - Calculate and interpret confidence intervals to estimate population values with precision - Implement modern tidyverse workflows alongside base R for efficient data preparation and analysis - Identify and correct sampling bias to ensure your statistical models remain valid This course begins with essential terminology and the mathematical foundations of probability, then transitions into practical R programming applications. Through structured written modules, you will explore real-world scenarios, analyze curated datasets, and practice writing clean R code to solve statistical problems. This course is designed for beginners, aspiring data analysts, and researchers who want to build a strong foundation in statistics using R, with no prior statistical experience required. Start reading today to unlock the power of statistical inference and make data-driven decisions with confidence.