<![CDATA[Statistical literacy is increasingly recognized as a fundamental competence for preservice mathematics teachers because it enables individuals to interpret data, reason under uncertainty, critically evaluate statistical information, and make informed judgments in diverse contexts. Despite its importance, research on statistical literacy has largely emphasized quantitative measurement of achievement, providing limited insight into how preservice teachers experience, interpret, and construct statistical meaning throughout their learning processes. This study explored preservice mathematics teachers’ statistical literacy by integrating quantitative performance data with qualitative interpretations of lived learning experiences through a sequential explanatory mixed-methods design informed by hermeneutic phenomenology. The participants were 36 preservice mathematics teachers enrolled in a statistics course at Universitas PGRI Silampari. Quantitative data were collected using a statistical literacy assessment measuring procedural competence, data interpretation, and contextual reasoning, while qualitative data were obtained through reflective questionnaires and semi-structured interviews. Descriptive statistical analysis and iterative thematic interpretation were employed to examine the data. The findings indicated that although most participants demonstrated satisfactory procedural competence, many encountered difficulties in interpreting statistical information, justifying conclusions, and connecting statistical results to meaningful real-world contexts. Qualitative evidence further revealed that participants’ understandings of statistics were strongly shaped by prior instructional experiences that emphasized formula application over interpretation and reasoning. These findings suggest that the development of statistical literacy involves not only procedural proficiency but also interpretative reasoning, reflective meaning-making, self-confidence, and engagement with contextualized learning experiences, highlighting the need for instructional approaches that support deeper statistical understanding in teacher education.]]>
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