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Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.
Hypothesis testing lets you ask questions about your datasets and answer them in a statistically rigorous way. In this course you'll learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. You'll gain a deep understanding of how they work, and the assumptions that underlie them. You'll also learn how different hypothesis tests are related using the "There is only one test" framework, and use non-parametric tests that let you side-step the requirements of traditional hypothesis tests. Throughout the course, you'll explore a Stack Overflow user survey, and a dataset of late shipments of medical supplies.
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Introduction to Hypothesis Testing
-Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.
Two-Sample and ANOVA Tests
-Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.
Proportion Tests
-Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.
Non-Parametric Tests
-Learn about the assumptions made by parametric hypothesis tests and see how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.
