This module introduces students to the concept of confidence intervals and their construction. It covers the t-distribution and related Excel functions (T.DIST and T.INV), providing a conceptual understanding of confidence intervals. Students learn about z-statistics and t-statistics and how to use them to construct confidence intervals for different business scenarios. The module provides a solid foundation for understanding statistical inference through hands-on exercises in Excel.

This module applies confidence interval concepts to real business scenarios. Students learn to calculate and interpret confidence intervals for population proportions, determine appropriate sample sizes for various research questions, and get an introduction to hypothesis testing. Through practical examples, the module demonstrates how confidence intervals help quantify uncertainty in business decision-making and how to choose sample sizes that balance precision with cost considerations.

This module teaches the logic and process of hypothesis testing as a critical business statistics tool. Students learn the four-step process for conducting hypothesis tests, understand the differences between single-tail and two-tail tests, and learn to perform hypothesis tests for population means and proportions. The module also covers Type I and Type II errors in hypothesis testing and strategies to minimize these errors, giving students practical skills to apply statistical testing to business problems.

This advanced module focuses on hypothesis tests that compare differences between means from different data sets. Students learn three types of difference-in-means tests: independent samples with equal variance, independent samples with unequal variance, and paired samples. The module covers various business applications of these tests and teaches students how to use Excel's built-in functions to perform them efficiently. By the end of the module, students can select and apply the appropriate test for different business scenarios.