Comparison of Parametric and Nonparametric Methods in Real-World Data Analysis

Abstract

Statistical analysis plays a critical role in interpreting real-world data, and the choice between parametric and nonparametric methods significantly influences the accuracy and reliability of results. Parametric methods rely on strong assumptions about the underlying data distribution, such as normality and homoscedasticity, offering high efficiency and interpretability when these assumptions are met. In contrast, nonparametric methods require minimal assumptions, making them ideal for skewed, ordinal, or heterogeneous data commonly found in real-world scenarios. This paper presents a comprehensive comparison of both approaches through theoretical discussion and practical application on diverse datasets, including income distribution, clinical trial data, and global temperature trends. The results highlight that parametric methods tend to perform well under controlled or ideal conditions, whereas nonparametric methods demonstrate greater robustness and flexibility, especially when data deviate from classical assumptions. By evaluating key statistical tasks such as estimation, hypothesis testing, and regression modeling—we show how each approach yields different insights depending on the data structure. The study emphasizes that no single method is universally superior; rather, effective analysis requires understanding the strengths, limitations, and appropriate contexts for each approach. This comparison provides practical guidance for researchers and analysts working with complex and imperfect real-world data.