📄 Abstract
Statistical literacy has become an essential component of higher education and research competence because students increasingly encounter, analyse, interpret, and communicate quantitative information in academic, professional, and digital environments. This systematic review examines the literature on students’ statistical literacy, with particular attention to their ability to understand statistical concepts, select appropriate statistical procedures, analyse data, interpret statistical outputs, critically evaluate evidence, and communicate research findings. The review synthesises evidence concerning statistical literacy, statistical reasoning, statistical thinking, statistical inference, statistical anxiety, attitudes toward statistics, statistical software, and contemporary approaches to statistics education. Existing research indicates that statistical literacy extends beyond computational proficiency and requires students to understand statistical concepts, recognise variability and uncertainty, select appropriate analytical procedures, interpret results within their substantive context, evaluate assumptions and limitations, and formulate defensible conclusions. Evidence also demonstrates persistent difficulties in understanding statistical inference, including p values, confidence intervals, sampling variability, statistical significance, effect sizes, and the distinction between association and causation. Meta-analytical evidence further indicates that statistics anxiety, self-efficacy, learning strategies, and attitudes can influence students’ experiences with statistics, although affective outcomes should not be equated with demonstrated statistical competence. Recent research also highlights substantial diversity in the conceptualisation and measurement of statistical ability. This diversity creates challenges for cumulative evidence synthesis and demonstrates the need for multidimensional and authentic assessments. The review identifies important gaps concerning authentic research tasks, statistical-tool selection, software-supported reasoning, AI-supported statistics learning, longitudinal development, disciplinary differences, and evidence from African and Nigerian universities. The review concludes that statistics education should move beyond computational training toward integrated development of statistical knowledge, reasoning, interpretation, critical evaluation, and communication.
🏷️ Keywords
📚 How to Cite:
Offor, Moses, Mohammed Salihu Abubakar , Ameh Joseph Ojoniko , STATISTICAL LITERACY AND THE USE OF STATISTICAL TOOLS IN RESEARCH: A SYSTEMATIC REVIEW OF STUDENTS’ STATISTICAL COMPETENCE IN NIGERIAN HIGHER EDUCATION , Volume 12 , Issue 9, September 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 446 - 462 , DOI: https://doi.org/10.36713/epra31556