Introduction to Inferential Statistics with R

Making decisions based on raw data can be risky without understanding the underlying patterns and uncertainties. This text-based course guides you through the core concepts of statistical inference, helping you draw confident conclusions from your data. You will learn how to transition from simple data description to making powerful, statistically backed claims about larger populations. By practicing with real-world scenarios, you will master the art of setting up statistical tests, calculating confidence intervals, and translating complex mathematical outputs into clear, actionable insights. What you'll learn: - Understand foundational concepts of probability, sampling distributions, and the Central Limit Theorem - Formulate and conduct hypothesis tests for both numerical and categorical data - Interpret p-values, significance levels, and confidence intervals accurately to measure uncertainty - Apply modern tidy statistical workflows in R using contemporary packages for clean, reproducible analysis - Report and communicate statistical findings clearly to non-technical stakeholders and clients The course begins with essential terminology and the mathematical foundations of probability before moving into practical programming. You will progress through step-by-step written analyses of categorical and numerical data, learning how to structure your code and interpret the results. This course is designed for beginners, aspiring data analysts, and researchers who want to build a strong foundation in statistics. No prior experience with R or advanced mathematics is required. Start your journey into data-driven decision-making and learn to analyze data with statistical confidence today.