Developing the ability to understand and use multiple representations is vital for deepening students' comprehension of statistical, probabilistic, and mathematical concepts. This skill enhances problem-solving by enabling learners to translate ideas across various forms, leading to more flexible and meaningful understanding. Representational competency is closely linked to communication, critical thinking, and the ability to engage with real-world data. Moreover, students' attitudes, emotions, and self-confidence significantly influence their motivation and success in these subjects. Strengthening these aspects in education can lead to more effective learning experiences and greater long term achievement in math and statistics. "Modes of Representation in Developing Statistical Thinking in Education" explores different modes of representations in teaching and learning statistical, probabilistic notions, and attitudes in developing statistical thinking in education. By bringing together contributions from global experts, the volume fosters interdisciplinary collaboration, inspires new research directions, and provides the knowledge and tools necessary to navigate the evolving landscape of statistics education. Covering topics such as artificial intelligence, mathematics education, and web tools, this book is an excellent resource for researchers, academicians, educators, policymakers, data scientists, and more.