Estimation and Hypothesis Testing: A Practical Statistical Guide

How do researchers draw confident conclusions from limited data? Understanding statistical estimation and hypothesis testing is the key to making data-driven decisions with mathematical rigor. This structured written course takes you from foundational probability concepts to key testing frameworks, enabling you to analyze datasets, construct confidence intervals, and validate scientific claims. You will learn to think like a statistician, evaluating evidence systematically and avoiding common analytical pitfalls. In this course, you will: Learn the core principles of point and interval estimation; Construct and interpret confidence intervals for means, proportions, and variances; Formulate null and alternative hypotheses for various scenarios; Perform critical classical tests, including z-tests, t-tests, chi-square, and ANOVA; Evaluate test results using p-values and significance levels; Understand modern statistical challenges like p-hacking and resampling basics. The course begins with essential definitions of parameters and statistics before progressing step-by-step through estimation methods and testing mechanics. Each concept is explained through clear text explanations, mathematical breakdowns, and practical scenarios. This course is designed for beginners in statistics, college students preparing for quantitative exams, and aspiring data professionals who want a solid theoretical foundation. No advanced mathematical background is required to start. Start reading today to build a rock-solid foundation in statistical inference.