Hypothesis Testing and Applied Statistical Analysis

Making sense of experimental results and data trends requires a structured approach to decision-making. Hypothesis testing provides the mathematical foundation needed to validate assumptions, assess statistical significance, and draw reliable conclusions from data. In this text-based course, you will develop a solid understanding of statistical testing methods. You will explore core theoretical concepts and apply them through clear written explanations, practical examples, and hands-on calculation walkthroughs. What you'll learn: - Understand null and alternative hypotheses, significance levels, and p-values - Formulate testable statistical hypotheses for real-world scenarios - Apply one-sample, two-sample, and paired t-tests correctly - Perform ANOVA and Chi-Square tests to evaluate multi-group categorical and numeric data - Interpret confidence intervals and effect sizes alongside statistical significance - Avoid common pitfalls such as Type I and Type II errors and p-hacking The course begins with foundational statistical definitions and probability principles before guiding you step-by-step through standard testing workflows and interpretation techniques. This course is designed for beginners, data analysts, researchers, and students who want a solid grounding in statistical inference without needing prior advanced mathematical training. Start reading today to build your confidence in statistical analysis.