I believe that statistical tests help us understand data and check whether the results are significant. Tools such as ANOVA are used to compare means between two or more groups at the same time, and t-tests are used to compare means between two groups. In related groups with a negative trend, the same subjects are tested at different times to see if there is a real decline in performance. On the other hand, a positive trend shows improvement over time, which is common in educational studies. For independent groups with a bivariate trend, researchers use factorial ANOVA or independent t-tests to see if there are differences between groups based on two variables. These tools help reduce bias, control variability, and support valid conclusions. With these tests, researchers can make decisions based on real data rather than assumptions and focus on the p-value. This is very important for obtaining reliable results in group comparison studies and also helps identify patterns, predict future outcomes, and design better strategies for learning, behavior, or other measured variables.