Data can be viewed from three different types: continuous, categorical, and time-to-event data analysis. For the first type, various statistical hypotheses testing (such as Wilcoxon Rank-Sum test, Kruskal-Wallis test, etc.) for continuous-type data have been discussed rigorously. In this research we give a comprehensive review of the multiple-sample continuous-type data testing methods. For the second type*, numerous categorical-type data analysis methods exist in the literature. For the third type, several methods have been discussed in the literature. We also provide the roadmap to which methods are available in the literature. In addition, the implementation of these methods in popular statistical software packages is also discussed. This article will be very helpful for researchers to determine which test or method is available to use in both clinical trials and epidemiology fields, both for the design phase of a prospective study, or retrospective study analysis.

Keywords: 2-sample t-test; One-way ANOVA; Satterthwaite d.f.; degrees of freedom; Welch ANOVA; Wilcoxon Rank-Sum Test; Kruskal-Wallis Test; paired t-test; Breslow-Day (BD) test; Cochran-Mantel-Hanszel (CMH) test; common odds ratio (OR); McNemar’s test; paired binary data; stratified data; Bowker’s test; symmetry test; stratified test; Stuart-Maxwell’s test; marginal homogeneity; Fisher’s Exact Test; Chi-Squared Test; Common Risk Difference (CRD); Homogeneous Stratum Effect (HSE); Kaplan-Meier curve; log-rank test; Cox regression; univariate; multivariate; adjusted factors; *presented on 2014 roundtable.