Hierarchical models are statistical frameworks that organize data at multiple levels, allowing for the analysis of complex structures. These models are particularly useful in situations where data is nested, such as students within schools or patients within hospitals. By accounting for the relationships between different levels, hierarchical models can provide more accurate estimates and insights. In a hierarchical model, parameters can vary across groups, enabling researchers to capture both individual and group-level effects. This approach is commonly used in fields like psychology, education, and healthcare, where understanding the influence of both individual characteristics and broader contexts is essential for effective analysis.