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This book is written for students who want to learn statistics as a way of thinking, not merely as a collection of formulas. Its philosophy is simple: before calculating, we must understand the question; before modelling, we must understand the design; and before interpreting results, we must understand what the data can and cannot justify. Statistical work begins with careful reasoning about populations, samples, variables, measurement, bias, uncertainty, and the logic of evidence. Formulas are important, but they are introduced as tools that serve scientific questions rather than as isolated mathematical recipes. Throughout the book, computation in R is used not to replace understanding, but to make statistical ideas visible: sampling variability can be simulated, assumptions can be checked, uncertainty can be quantified, and misleading conclusions can be exposed. The book also connects classical statistical thinking to modern data science, machine learning, and AI, where prediction, generalisation, validation, and trustworthiness all depend on the same foundations: good design, representative data, transparent assumptions, and honest communication of uncertainty. The aim is to help readers become careful statistical thinkers who can move from real-world questions to defensible evidence, from data to interpretation, and from computation to responsible decision-making.

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