Introduction to Statistical Inference for Data Decisions

Every day, organizations make critical decisions based on data, but how do we know if our findings are truly significant or just random noise? Statistical inference provides the mathematical framework to turn raw data into reliable, actionable insights. This text-only course guides you from absolute beginner to confidently analyzing datasets and drawing valid conclusions. You will learn how to estimate population parameters, perform hypothesis testing, and apply modern resampling techniques to real-world scenarios. What you'll learn: • Understand foundational concepts of probability distributions, samples, and populations • Formulate and conduct hypothesis tests to validate business and scientific assumptions • Calculate confidence intervals to estimate population values with precision • Apply modern resampling methods, including bootstrapping, to analyze complex data • Design and interpret A/B tests to measure the impact of product and website changes • Identify common statistical pitfalls, such as p-hacking and selection bias, to ensure integrity. The course begins with essential terminology and the core mechanics of probability. From there, you will progress through step-by-step written explanations and practical scenarios, mastering hypothesis testing, estimation, and modern digital experimentation techniques. This course is designed for aspiring data analysts, product managers, and beginners with no prior background in advanced statistics. Start reading today to unlock the power of data-driven decision-making.