When would you use a chi-square test, and what does it measure?

During my final year at college, I worked on a group project where we conducted a survey to analyze student preferences for online versus in-person classes. We collected responses from over 200 students and wanted to see if the preference varied by major. To do this, we used a chi-square test to analyze our data, specifically testing the relationship between students' majors and their class format preferences. After calculating the chi-square statistic, we found a significant association, indicating that students in certain majors preferred online classes more than others. This experience not only helped me understand the chi-square test practically but also taught me the importance of data-driven decision-making.

While volunteering for a local non-profit that focuses on community health, I helped with a project assessing the effectiveness of health workshops. We distributed questionnaires to attendees and non-attendees to measure their health knowledge before and after the workshops. To analyze the data, we used the chi-square test to determine if attendance at the workshops significantly influenced changes in health knowledge. This experience reinforced my understanding of how statistical tests could provide insights into community engagement and effectiveness of outreach programs.

In my first job as a data analyst at a small marketing firm, I was tasked with analyzing customer feedback data. One of the projects involved evaluating whether customer satisfaction ratings were related to the type of product purchased. I applied a chi-square test to examine the relationship between product categories and satisfaction levels. The results showed a significant correlation, which led the marketing team to tailor their strategies for different products based on customer feedback. This experience highlighted the practical application of statistical tests in influencing business decisions.