How do you determine which statistical test to use for a given dataset?

During my internship at a marketing firm, I was tasked with analyzing survey data collected from customers. The survey included both categorical responses, like 'yes' or 'no' for satisfaction, and numerical ratings on a scale of 1 to 10. To determine the appropriate statistical test, I first identified the type of data I was working with. Since I wanted to compare the satisfaction levels across different categories of customers, I decided to use a Chi-square test for the categorical data and an ANOVA test for the numerical ratings. This experience taught me the importance of understanding the data types and the research questions before selecting a statistical test.

As a volunteer for a local charity, I helped organize a fundraising event and later analyzed the results. We had two groups: one that received promotional materials and one that didn’t. To see if the promotional materials had an impact on the amount raised, I used a t-test to compare the means of the two groups. I learned how to choose the t-test because our data was continuous (the amount of money raised) and we had two independent groups. This practical application further solidified my understanding of how to select a statistical test based on the dataset characteristics.

In my first job as an analyst, I was responsible for evaluating employee performance metrics. I had access to various datasets, including employee satisfaction scores and productivity rates. To analyze the relationship between these two variables, I chose to use Pearson’s correlation coefficient, as both variables were continuous and normally distributed. This experience reinforced my ability to assess the data at hand and choose the appropriate statistical test to draw meaningful insights from the analysis.