In your experience, how do you determine whether to use a t-test or a chi-square test for analysis?

In a psychology course, I conducted a project analyzing survey data on study habits among students. I gathered responses from 100 students about their study methods and grades. To determine if there was a significant difference in exam scores between students who studied alone versus those who studied in groups, I used a t-test. This helped me compare the means of two continuous variables. The results indicated that group study had a positive impact on exam performance, which I discussed in my presentation, highlighting the importance of study environments.

During my volunteering experience with a local health initiative, I helped analyze community health data. We collected categorical data on whether residents engaged in regular exercise and their self-reported health status. To see if there was an association between exercise and health status, I used a chi-square test. This analysis revealed that those who exercised regularly reported better health outcomes, which we used to advocate for more community fitness programs. This experience taught me how to apply statistical methods in real-world scenarios to drive positive change.

In my first job as a research assistant, I worked on a project analyzing customer preferences for a new product. We had collected both categorical and continuous data through surveys. For comparing customer satisfaction scores (continuous data) across different age groups, I used a t-test to see if there were significant differences. Meanwhile, for analyzing the relationship between customer satisfaction and purchase frequency (categorical data), I utilized the chi-square test. This experience solidified my understanding of when to use each test and improved my data interpretation skills.