Foundations of Inferential Statistics: Parametric and Non-Parametric Methods

Making data-driven decisions requires more than just describing the numbers you have; you need to understand what those numbers say about the larger population. This text-based course demystifies the core principles of statistical inference, helping you confidently draw conclusions from data. You will master the fundamental logic of hypothesis testing and learn how to choose the right statistical approach for your analysis. By reading through clear explanations and practical examples, you will transition from a basic understanding of data to performing robust statistical reasoning. What you'll learn: Understand the foundational concepts of populations, samples, and sampling distributions; Formulate null and alternative hypotheses and interpret p-values with confidence; Apply parametric tests including t-tests, ANOVA, and Pearson correlation; Implement non-parametric alternatives such as Mann-Whitney U, Wilcoxon, and Kruskal-Wallis tests; Practice selecting the correct statistical test based on your data structure and assumptions; Recognize modern best practices in statistical reporting, including effect sizes and confidence intervals. The course begins with essential terminology and the basic logic of probability and sampling, then guides you step-by-step through parametric and non-parametric testing frameworks with clear, real-world scenarios. This course is designed for beginners, students, and aspiring data analysts who want a conceptual, math-friendly introduction to statistics with no prior advanced mathematics or derivation experience required. Start reading today to unlock the power of statistical decision-making.