Selecting the appropriate statistical test is a critical step in quantitative research, as it ensures that the data is analyzed correctly and that the conclusions drawn are valid. The University Library's Sage Research Methods "Which Stats Test?" tool, combined with guidance from Salkind and Frey (2019), provides a structured approach to selecting the most suitable test based on the nature of the variables and the research questions posed. Using the "Which Stats Test?" tool, I evaluated the mental health scores (continuous) of my chosen variables and the type of work environment (categorical). The tool suggested tests such as the independent samples t-test or ANOVA, which aligns with the guidance provided in Figure 9.1 of Salkind and Frey (2019). The process involved identifying independent and dependent variables, determining their level of measurement (nominal, ordinal, interval, or ratio), and evaluating whether the data met assumptions like normality and homogeneity of variance. Variable types have a significant impact on the choice of statistical tests. Continuous variables often require parametric tests, such as the t-test or regression analysis, while categorical variables may necessitate chi-square tests or logistic regression. The number of groups compared, sample size, and distribution shape also influence test selection (Field, 2018). For instance, if assumptions for parametric tests are violated, non-parametric alternatives, such as the Mann-Whitney U test