Statistical tests for dependent and independent solutions are the main ways for testing the fit between a hypothesis and the data in many fields of science. This paper is focused on reviewing these tests, analyzing the theoretical principles that these tests support and their concrete application. The tests that include independent solutions are used when two variables are not correlated, and their goal is to review if there are significant differences between different groups. Two main tests for these kinds of problems are the Chi- square test for independence, a test which assesses the relationship between two categorical variables, and the t- test for independent groups, which is used for comparison of the means of two independent groups. While, tests for dependent variables, analyze related variables, and are useful to review the changes between the same measurements over time, or to review the influence of a variable on another. The t-test for dependent variables and the linear regression model are examples of dependent solutions, which are used to compare measurements before and after this test, or to analyze the relationship between a dependent variable and several independent variables. This paper illustrates the use of these tests on different research studies, including medical research, social research, and economic research, emphasizing the importance of choosing the right test in order to achieve accurate and reliable results. In conclusion, the use of these statistical methods is fundamental to achieving valid conclusions and support in making decisions in various fields of study.
Ramani et al. (Thu,) studied this question.