The purpose of this study is to structurally analyze "misconceptions," which constitute one of the greatest obstacles to statistical literacy skills that lie at the center of decision-making processes in knowledge-based societies, to reveal the origins of these misconceptions, and to offer contemporary pedagogical strategies and solution proposals for their remediation. This study is a qualitative conceptual analysis and a literature review. In this direction, the cognitive conflicts experienced by students during the transition from a deterministic mathematical way of thinking to a probabilistic and predictive statistical way of thinking are addressed within the framework of Kahneman and Tversky’s (1974) "Heuristics," Konold’s (1989) "Outcome Orientation," and Shaughnessy’s (1992) "Variation" theories. As a result of the literature analysis, the most common misconceptions encountered in statistics education are classified into four main categories. It has been observed that traditional and calculation-oriented methods in statistics education remain insufficient in providing conceptual depth and eliminating change-resistant misconceptions. In this context, the study advocates for creating a "cognitive conflict" in the classroom in accordance with the Conceptual Change Model developed by Posner et al. (1982), concretizing abstract concepts (e.g., Central Limit Theorem) through dynamic simulations and visualization tools (e.g., R-Shiny, GeoGebra), and constructing lessons on real data sets. Regarding measurement and evaluation processes, the use of "reason-questioning" two-tier diagnostic tests rather than formula calculations, and organizing in-service pedagogical content knowledge training for educators are recommended.