This course provides a comprehensive introduction to hypothesis testing, one of the most fundamental techniques in inferential statistics. The course is designed to guide students through the process of making data-driven decisions by evaluating claims about populations based on sample data. Beginning with the essential concepts of null and alternative hypotheses, students will learn how to construct testable statements about population parameters and will explore the reasoning behind the formulation of these hypotheses. The course will emphasize the critical role of hypothesis testing in drawing conclusions in various real-world contexts, from scientific research to business decision-making.

A key focus of the course will be the framework for making decisions using sample data. Students will develop a deep understanding of statistical significance and the logic behind rejecting or failing to reject a null hypothesis. They will also become familiar with the critical concepts of Type I and Type II errors, learning how to interpret p-values and confidence levels, and gaining insights into how these affect conclusions in hypothesis testing. Throughout the course, students will engage with one-sample and two-sample t-tests, z-tests for population proportions.

By the end of the course, students will have the tools and knowledge to apply hypothesis testing to a range of research and business problems. They will also be equipped to critically evaluate the results of hypothesis tests reported in academic studies and the media. With an emphasis on both theoretical understanding and practical application, the course prepares students to confidently use hypothesis testing in their future academic and professional endeavors.