This chapter focuses on inferential data analysis, which builds on the foundational work of descriptive analysis covered in Chap. 20 . While descriptive analysis organizes and summarizes data, inferential analysis goes further to make predictions and generalizations about the population based on sample data. In inferential analysis, researchers test hypotheses using samples and make inferences about the population. The main inferential analysis includes means comparison, determining relationships and making predictions. In comparing means, we use a t-test or analysis of variance (ANOVA) depending on the number of means to be compared. T-tests are used for comparing means of two groups, while ANOVA is used for comparing more than two means. T-tests are of three categories: One sample t-test, independent t-test, and correlated t-test. In ANOVA too, there are various types depending on the number of factors (number of independent variables). For instance, we have “one-way ANOVA” with one independent variable, “Two-way ANOVA” with two independent variables, and “Three-way ANOVA” with three independent variables. In testing relationships, the scale of measurement is considered. If dealing with two categorical variables, the Chi-square statistic is used. If the variables are ranked, Spearman rank Correlation is employed, while for two quantitative variables, Pearson product-Moment is used. In making predictions and regression, the main ones include using simple linear and multiple regression models.