Comparison of Two Independent Samples

Abstract Statistical analysis is essential for clinicians to interpret research and apply findings to practice. When comparing two independent samples, selecting the correct test depends on the underlying data assumptions. This study outlines the principles and application of the two-sample t -test, the Wilcoxon Mann–Whitney U test, and the chi-square test. The t -test evaluates differences between means when data are normally distributed with equal variances, whereas the Wilcoxon Mann–Whitney U test serves as a nonparametric alternative for skewed distributions or unequal variances by analyzing ranked data. In contrast, the chi-square test is used for categorical variables to assess independence between groups. Each method yields a P -value, which reflects the probability of observing the data under the null hypothesis but does not directly convey clinical relevance. Understanding test assumptions—such as normality, variance homogeneity, independence, and adequate sample size—is critical for valid interpretation. Additionally, reliance on P -values alone can be misleading without considering effect size and study design. Confidence intervals and randomization-based methods offer complementary insights. By clarifying the assumptions, strengths, and limitations of these tests, this article enables clinicians to select appropriate statistical tools and make more meaningful inferences from research, ultimately supporting better decision-making in clinical practice.