<![CDATA[Background. The rapid development of information technology and the increasing use of data across various domains require students to be able to understand and critically interpret statistical information. Statistical literacy has become an essential competency in higher education, particularly in contexts that demand data analysis and interpretation of research findings. However, previous studies indicate that students still experience difficulties in understanding statistical concepts and interpreting statistical information appropriately. Purpose. This study aims to explore the statistical literacy of mathematics education students after completing a mathematical statistics course, with a particular focus on how they experience and construct meaning of statistical concepts. Method. This study employed a qualitative approach with a phenomenological design to examine students’ lived experiences in learning statistics. The participants were mathematics education students who had completed a mathematical statistics course. Data were collected through a statistical literacy test consisting of 15 items used as an elicitation tool, followed by semi-structured interviews to explore students’ meaning-making processes. Data were analyzed using thematic analysis to identify patterns in how students understand and interpret statistical concepts and information. Results. The findings reveal that students’ statistical literacy is predominantly characterized by procedural understanding, where students are able to execute statistical procedures but struggle to interpret results and critically evaluate research findings. More importantly, the study identifies a pattern of understanding conceptualized as surface statistical thinking, in which students rely on mechanical procedures, exhibit limited knowledge transfer, and demonstrate fragmented conceptual understanding. These findings highlight that students’ difficulties are not merely related to ability levels, but are rooted in how they experience and make sense of statistics learning. Conclusion. This study contributes theoretically by proposing surface statistical thinking as a phenomenological construct that explains the gap between procedural performance and conceptual understanding in statistical literacy. Therefore, statistics instruction in higher education should be designed to promote conceptual understanding through contextual, reflective, and data-driven learning, while integrating data literacy and critical thinking to better prepare students for the demands of the data-driven and artificial intelligence era.]]>
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