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COMPARING TWO INDEPENDENT GROUPS
Essay 5: Comparing Two Independent Groups
School of Behavioral Sciences, Liberty University
Author Note
I have no known conflict of interest to disclose.
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COMPARING TWO INDEPENDENT GROUPS
Essay 5: Comparing Two Independent Groups
In statistical analysis, the process of comparing two distinct groups is a critical method
with far-reaching implications (Warner, 2021a). This essay delves into the foundational
assumptions necessary for testing independent samples, specifically focusing on normality,
homogeneity of variance, and independence of errors (Warner, 2021a). Not adhering to these
assumptions can significantly impact the results of the analysis (Kim & Park, 2019; Medugu et
al., 2021; Saccenti, 2023).
A central aspect of this discussion is whether a p-value can ever be zero. This necessitates
an exploration into the nuances of statistical significance and how p-values are interpreted in
research (Marshall & Lindsay, 2017; Warner, 2021a). P-values are essential in hypothesis testing
as they provide evidence against the null hypothesis. A smaller p-value indicates stronger
evidence against the null hypothesis and is a crucial factor in determining statistical significance
(Andrade, 2019). This analysis has two main focuses: evaluating the assumptions in independent
sample testing and understanding the effect sizes and factors that influence t-values in statistical
studies (Sá et al., 2022). The aim is to equip researchers with the knowledge to effectively
address the challenges of comparing two independent groups, thereby enhancing the quality of
research in various statistical fields (Marshall & Lindsay, 2017).
Assumptions and Significance of Independent Samples Testing
There are several assumptions for the use of an independent samples t test. State each of
