Comparison of Empirical and Classical Probabilistic Reasoning In Solving High School Mathematics Problems: A Systematic Literature Review - Endang Setyowati Mas Ula; Abdussakir; Elly Susanti (Universitas Muhammadiyah Malang, 2026)
Probabilistic reasoning is a fundamental competency in high school mathematics education, yet the comparative strengths and limitations of empirical (frequency-based, real-world data) versus classical (a priori, theoretical) probabilistic reasoning approaches remain understudied in the Indonesian secondary school context. This Systematic Literature Review (SLR) synthesises 25 peer-reviewed studies published from 2021 to 2025, supplemented by four methodological anchor studies (2012–2024), identified through a PRISMA 2020-compliant search of Scopus, ERIC, Google Scholar, and SINTA databases. Studies were screened against predefined inclusion/exclusion criteria and assessed for quality using the Mixed Methods Appraisal Tool (MMAT). Findings indicate that empirical reasoning significantly outperforms classical reasoning in probabilistic literacy and critical reading of statistical information from media sources (66.7% vs. approximately 15%), while classical reasoning is superior in procedural accuracy and conceptual depth, particularly in applying Bayes' theorem (58% vs. 10.6%). The transposed conditional bias affects more than 50% of participants in empirical contexts, while the equiprobability fallacy is more prevalent in classical settings. Both approaches yield comparable proportions of students reaching the reflective use metacognitive level (approximately 36%), but through distinct cognitive pathways. Integration of both approaches within non-routine problem-based learning environments is recommended to develop holistic probabilistic competency in high school studens