This chapter offers an overview of quantitative method design and data analysis within management research. It details key analytical stages—from data cleaning to multivariate techniques and interpretation—whilst addressing the advantages (objectivity, generalisability, replicability) and disadvantages (lack of detail, rigidity) of quantitative methods, further noting how AI augments these methods. The core deliberates widely utilised parametric inferential statistics, including t-tests, ANOVA variants (One-Way, ANCOVA, Two-Way, Repeated Measures), MANOVA, MANCOVA, Pearson Correlation, Multiple Regression Analysis, Logistic Regression Analysis, and Discriminant Analysis, outlining their assumptions, applications, and interpretations. Subsequently, it covers crucial non-parametric tests—Chi-square, Fisher’s Exact, McNemar’s, Sign, Wilcoxon Rank Sum, Cochran Q, Kruskal-Wallis H, and Friedman’s and Spearman’s Rank Correlation—each enlightened with its purpose and fundamental requirements. Overall, the chapter acts as a practical guide to selecting and utilising appropriate statistical techniques for testing varied research hypotheses in management discipline.