Exploring the Use of Nonparametric Methods in Handling Non-Normal and Skewed Data Distributions Across Research Fields

Posted: Jun 16, 2014

• James Gonzalez
• James Young
• Jack Smith

Abstract

The assumption of normality underpins many traditional statistical methods, serving as a foundational requirement for parametric tests that dominate research across numerous disciplines. However, real-world data frequently violate this assumption, exhibiting various forms of non-normality including skewness, kurtosis, multimodality, and the presence of outliers. This fundamental mismatch between statistical assumptions and empirical reality poses significant challenges for researchers seeking valid inferences from their data. The consequences of violating normality assumptions can be severe, leading to increased Type I and Type II error rates, biased parameter estimates, and ultimately, compromised research conclusions. Despite widespread recognition of these issues, many researchers continue to default to parametric methods due to familiarity, computational convenience, or insufficient awareness of robust alternatives. Nonparametric statistical methods offer a powerful alternative approach that does not rely on strict distributional assumptions. These methods are particularly valuable when dealing with small sample sizes, ordinal data, or distributions that exhibit substantial deviations from normality.