International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/4127 2020 2020 Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand Sasiwimon Sasiwimon Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand Ronnason Chinram Classical statistical methods assume that data are precise and free from uncertainty, which may not hold in many real-world applications. Neutrosophic statistics provides a flexible framework for handling indeterminacy, vagueness, and inconsistency in data. In this paper, we propose a new formulation of one-way analysis of variance (ANOVA) within the neutrosophic framework. The method treats membership, indeterminacy, and non-membership components separately, with explicit F -tests for each, and employs a maximum-based decision rule to determine significance. We also compare the proposed method with the classical one-way ANOVA. The results demonstrate that the neutrosophic ANOVA is more sensitive in detecting group differences, particularly in cases where the classical approach yields smaller F -values and may fail to reject the null hypothesis. These findings highlight the potential of neutrosophic ANOVA as a more robust alternative to classical ANOVA for analyzing data with inherent uncertainty and indeterminacy. 2026 2026 246 259 10.54216/IJNS.270122 https://www.americaspg.com/articleinfo/21/show/4127