Practical Inferential Statistics for Data Decisions

How do we make confident decisions about an entire population when we can only analyze a small sample? Inferential statistics provides the essential mathematical framework to turn raw sample data into reliable, actionable insights. This text-only course guides you from foundational probability concepts to advanced hypothesis testing. You will learn how to analyze sample data, calculate margins of error, and determine whether your findings represent real trends or just random noise. What you'll learn: - Understand key concepts of probability, sampling distributions, and the Central Limit Theorem. - Calculate confidence intervals to estimate population parameters with precision. - Perform hypothesis testing, including t-tests, ANOVA, and chi-square tests, to validate assumptions. - Analyze relationships between variables using correlation and simple linear regression. - Apply statistical principles to modern scenarios like digital A/B testing and data science experiments. - Interpret p-values and statistical significance correctly to avoid common analytical pitfalls. You will start with core definitions and probability basics before moving step-by-step through testing methodologies, supported by clear written explanations and practical calculation scenarios. Designed for beginners, this course requires no prior background in statistics or advanced mathematics. Start reading today to unlock the power of statistical inference and elevate your analytical skills.