Statistical inference allows us to draw meaningful conclusions about a population by analysing a representative sample. This unit covers foundational probability concepts, providing the framework for using sample data to make inferences about the broader population. It then explores the theory and application of classical statistical inference techniques to quantify uncertainty and make informed decisions. The unit also introduces the Bayesian approach, which combines prior knowledge with sample data for a more holistic, subjective analysis, especially useful in areas with limited data or significant prior knowledge. The focus is on building a strong conceptual understanding, with practical examples to reinforce theory and highlight real-world relevance.
Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure
