In my opinion, the Pearson correlation is used to determine if two numerically measured variables, such as age and test scores, are linearly related. However, it only works well with continuous, normally distributed data, meaning data that follows a bell-shaped curve. If the data is not normal or is based on ranks, the Spearman's rho test is a better option. This test also shows if there is a relationship between two things and is more flexible with the data. On the other hand, the chi-square test is used to see if there is any connection between categories, such as type of pet and favorite hobby. It's useful for surveys and questionnaires. Following the correct assumptions of each test helps us to better understand the data, avoid mistakes, and get results we can trust when analyzing information. In the end, knowing when and how to use each test helps us make better decisions based on data.