Provides a full overview of the statistical principles of modern stereology, with the main focus on design-based stereologyOffers extensive support for statistical consultants via examples, applications, and "Advice to Consultants" sectionsContains numerous literature references, along with bibliographic notes and nearly 150 illustrationsDetails isotropic, vertical, or local sampling designs for estimating stereological parameters such as volume, surface area, particle number and spatial distribution Stereology, or quantitative microscopy, is a basic research tool in science and technology. The emergence of design-based methods has greatly increased the power, flexibility, adaptability, and scope of stereology applications, establishing a closer connection between statistics and quantitative microscopy. Despite its scientific importance, modern stereology remains largely unknown to the statistical community, with valuable information either widely scattered or inaccessible to newcomers to the field. Now is the perfect time for a book that enables biostatisticians and statistical consultants to give beneficial advice to researchers in microscopy.Stereology for Statisticians sets out the principles of stereology from a statistical viewpoint, focusing on both basic theory and practical implications. This book discusses ways to effectively communicate statistical issues to clients, draws attention to common methodological errors, and provides references to essential literature. The first full text on design-based stereology, it opens with a review of classical and modern stereology, followed by a treatment of mathematical foundations such as geometry, probability, and statistical inference. The book then presents core techniques, including estimation of absolute geometrical quantities, relative quantities, and statistical inference for populations of discrete objects. The final chapters discuss implementing techniques in practical sampling designs, summarize understanding of the variance of stereological estimators, and describe open problems for further research.