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Literature Review Volume 9 Issue 1
Difficulties and practical issues in statistical analysis in research with small sample size: a review
Loso Judijanto
Received: March 02, 2026 | Published: April 10, 2026
Citation: Judijanto L. Difficulties and practical issues in statistical analysis in research with small sample size: a review. Open Access J Sci. 2026;9(1):132-144. DOI: 10.15406/oajs.2026.09.00297
Abstract
Research conducted with limited sample sizes remains common across empirical disciplines, yet persistent concerns surround the validity and stability of statistical inference under such conditions. Small samples are frequently associated with reduced power, unstable estimates, and distorted error rates, raising questions about the reliability of reported findings. This study systematically synthesizes contemporary empirical evidence to identify the dominant statistical challenges and to evaluate methodological strategies proposed to mitigate inferential risks in small-sample research. The study employed a Systematic Literature Review design. Data were collected through structured searches in the Scopus database, followed by multi-stage screening based on relevance, publication year (2020–2025), language, and open-access criteria, resulting in 39 peer-reviewed articles for analysis. Data extraction focused on reported statistical limitations, empirical performance indicators, and recommended analytical adaptations. The analysis applied thematic synthesis and quantitative aggregation of reporting frequencies across the included studies. The results identify six interrelated thematic categories emerging from the synthesis of the reviewed studies: low statistical power and effect-size distortion, instability of parameter estimates, violations of distributional assumptions, elevated risks of Type I and Type II errors, model overfitting, and limited generalizability. In addition, the literature consistently highlights a complementary theme concerning proposed methodological adaptations intended to address these constraints under small-sample conditions. In conclusion, small-sample research entails systematic inferential vulnerabilities requiring rigorous analytical justification and transparent reporting. Future research should prioritize comparative simulations and integrated methodological frameworks to refine statistical guidance under limited data conditions.
Keywords: small sample size, statistical inference, systematic literature review, statistical power, methodological adaptation
©2026 Judijanto. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.