Most textbooks for biostatistics only explain each individual statistical test with its mathematical formula. However, it is crucial to understand the relationships among the statistical methods and to properly integrate the individual methods to effectively apply them to real clinical research settings. The choice for valid statistical tests greatly depends on the dependency of the sample and the number of independent variables in the analyses as well as the measurement scale of dependent variables and independent variables. In this report, many statistical tests such as the two sample t-test, ANOVA, non-parametric tests, chi-square test, log-rank test, multiple linear regression, logistic regression, mixed model, and Cox regression model are addressed through hypothetical examples. The key for a successful analysis of a clinical experiment is to adopt suitable statistical tests. This study presents a guideline to clinical researchers for selecting valid and powerful statistical tests in their study design. The choice of suitable statistical tests increases the reliability of analytical results and therefore the possibility of accepting a researcher's clinical hypothesis. The proposed flowchart of appropriate tests of statistical inference will be of help to many clinical researchers to their study.