Explore the world of Hypothesis Testing in R, a statistical technique that allows you to ask meaningful questions about your datasets and obtain statistically sound answers. This comprehensive course will equip you with the knowledge and skills to effectively utilize common tests such as t-tests, proportion tests, and chi-square tests. Delve into the inner workings of these tests and gain a profound understanding of the underlying assumptions. Additionally, you will discover the interconnectedness of various hypothesis tests through the There is only one test framework. Furthermore, you will learn about non-parametric tests that offer alternatives to traditional hypothesis tests, bypassing their specific requirements. Begin your journey by understanding the significance and utility of hypothesis testing in R, while simultaneously grasping essential concepts along the way. Uncover the power of t-tests in comparing means between two groups and the versatility of chi-square tests in comparing observed and expected results. As you progress, you will unravel the intricate relationships between different hypothesis tests, exploring crucial aspects such as randomness, independence of observation, and sample sizes. Upon completion of this course, you will possess a profound comprehension of hypothesis testing in R and the ability to discern the appropriate tests for your data. Throughout the course, you will analyze real-world datasets, including a Stack Overflow user survey and a dataset on late shipments of medical supplies. Embark on this enlightening journey and enhance your statistical prowess with Hypothesis Testing in R.