This book delves into the authors? work toward deeper understanding of statistical inference in terms of reasoning with uncertainty and meaningfulness of probabilistic inferential output. Focusing on a valid, prior-free probabilistic inferential framework called inferential models, the authors explain how to first identify the underlying source of uncertainty as an integral part of statistical modeling and then make probabilistic inference by calculating the predictable quantity in a statistically accurate way.