As a college student and future professional, mastering variance analysis and mean-comparison tests is essential to ensure the viability of any research. Previously, I had a limited sense of how standard deviation and variance measure data dispersion; now I recognize that these parameters are critical for assessing group homogeneity before applying an ANOVA. For example, in a study with pre- and post-tests, it’s important first to check whether the variances across groups are comparable, and then to apply either a paired t-test or a repeated-measures ANOVA as needed to determine if observed mean differences reflect true effects rather than chance. In studies with independent samples, we use an independent samples t-test or a one-way ANOVA to compare means and establish whether any positive and negative trend is statistically significant. By applying these methods correctly, we strengthen our study’s internal validity and gain confidence in interpreting the results.