How would you determine which statistical test to use when analyzing a dataset?

In my statistics course, I worked on a project where we analyzed survey data to understand student preferences for online versus in-person classes. We collected responses from 200 students, asking about their favorite mode of learning. Since our data was categorical (responses were either 'online' or 'in-person'), we decided to use a Chi-Square test to assess whether the preferences were significantly different across various demographics. This experience taught me the importance of matching the statistical test to the data type, ensuring our conclusions were valid.

During my time volunteering at a community health organization, we conducted a health survey to assess lifestyle choices among local residents. We gathered both categorical data (e.g., smoking status) and continuous data (e.g., age, BMI). To analyze the effects of age on BMI, I suggested using a t-test to compare means between smokers and non-smokers. This hands-on experience reinforced my understanding of choosing the right test based on the type of data and the specific questions we wanted to answer.

In my internship at a marketing firm, I was tasked with analyzing sales data to identify trends over the past year. We had a dataset with monthly sales figures and promotional activities. I recognized that to compare the sales before and after promotions, an ANOVA test would be appropriate since we were dealing with multiple groups (sales figures across different months). This real-world experience helped me understand how to apply various statistical tests to draw meaningful insights and support data-driven decisions.