How would you approach selecting an appropriate statistical test for a given dataset?

In my statistics class, I worked on a project where we analyzed survey data collected from students about their study habits and academic performance. We first defined our research question: 'Is there a relationship between the number of hours studied and students' GPA?' After collecting the data, we categorized it into numerical (study hours and GPA) and categorical variables (like study methods). Since both our main variables were numerical and we wanted to see if they correlated, I decided to use Pearson's correlation coefficient. I ensured the data met the test's assumptions, such as normality. This method helped us conclude that there was a significant correlation, which we presented in our final report. My approach included careful consideration of the data type and the hypothesis, which is essential for making accurate statistical inferences.

While volunteering at a local health organization, I helped analyze data from a health survey conducted in our community. We wanted to compare the average health scores of two different age groups. I gathered the data, which was a mix of categorical (age group) and continuous variables (health scores). Since we were comparing means between two independent groups, I opted for an independent t-test. I ensured to check the assumptions, such as the normality of the data and equal variances. This experience taught me the importance of selecting the appropriate test based on the type of data and the specific questions we aimed to answer. It was rewarding to see how our findings could inform better health initiatives in the community.

In my first job as a junior analyst, I was tasked with analyzing customer feedback data for a product launch. We had collected both qualitative and quantitative data through surveys. Our goal was to determine if customer satisfaction differed based on demographic variables like age and location. Given the mixed data types, I had to be strategic in my approach. I used chi-square tests for categorical variables and ANOVA for comparing means across different groups. This experience highlighted the importance of understanding the underlying assumptions of each test and considering the data's distribution. By carefully selecting the appropriate statistical tests, we provided actionable insights to the marketing team, which ultimately helped improve our product positioning.