Statistical Hypothesis Testing for Data Science

In data science, making decisions based on raw numbers alone can lead to costly mistakes. Understanding how to mathematically validate your findings using statistical hypothesis testing is the key to turning raw data into reliable, actionable insights. This text-based course guides you from foundational probability concepts to executing and interpreting essential statistical tests. You will start by learning core terminology, understanding the difference between null and alternative hypotheses, and exploring how to set up your tests correctly. From there, you will explore significance levels, p-values, and how to avoid critical errors in your analysis. To ensure your skills are modern and relevant, you will also learn how to apply these statistical concepts to modern A/B testing frameworks and computational simulation methods. What you will learn: - Understand the core concepts of null and alternative hypotheses, significance levels, and p-values - Identify and mitigate Type I and Type II errors in your statistical analysis - Choose the correct statistical test, including t-tests, ANOVA, and Chi-Square tests, for your data type - Interpret test results accurately to make data-driven business decisions - Apply hypothesis testing principles to modern web-based A/B testing scenarios - Practice analyzing sample datasets through written step-by-step walkthroughs This course begins with essential definitions and foundational probability theory before moving into practical test selection and interpretation. It is designed entirely for beginners with no prior background in advanced statistics or programming. Start reading today to build a solid foundation in statistical decision-making.