Inferential Statistics and Probability for Data Decisions

How do we make confident decisions about an entire population when we only have access to a sample of data? Understanding inferential statistics is the key to unlocking these insights and making reliable predictions. This text-only course guides you through the core principles of probability and statistical inference. You will transition from simply describing data to drawing meaningful, mathematically sound conclusions, preparing you for modern data analysis and decision-making roles. What you will learn: 1. Understand foundational probability distributions and how they model real-world scenarios. 2. Master sampling distributions and the significance of the Central Limit Theorem. 3. Estimate population parameters with confidence intervals to quantify uncertainty. 4. Formulate and conduct hypothesis tests to validate data-driven claims. 5. Apply statistical concepts to modern scenarios like A/B testing. 6. Analyze error types and statistical power to ensure robust experimental designs. The course begins with essential terminology and probability foundations before progressing to sampling, interval estimation, and practical hypothesis testing frameworks. Designed for beginners and aspiring data professionals, this course requires no prior advanced mathematics experience. Start reading today to build a strong analytical foundation and make decisions backed by statistical proof.