The book "General theory of statistics: a textbook for universities in the direction and specialty" by I. and . Eliseeva and M. M. Yuzbasheva, prepared under the editorship of I. and . Eliseeva is a fundamental manual that will become an indispensable companion for students, teachers and professionals seeking to understand the basics of statistics more deeply. This book is not just a textbook, but a real guide to the complex and exciting world of numbers, probabilities and data analysis, revealing to the reader the wealth of methods and concepts underlying modern statistical science. What makes the General Theory of Statistics so attractive and unique? First, it covers a wide range of theoretical aspects, starting with classical concepts and ending with modern approaches, which allows students and specialists to get a holistic view of the discipline . Author's style and . and . Eliseeva and M. M. Yuzbasheva is distinguished by clarity, logic and accessibility, which is especially important for those who are just starting their acquaintance with statistics or want to systematize the already existing knowledge. The book deals in detail with the main topics - from probabilistic models and estimation methods to sampling theory and hypotheses, which makes it a valuable source for both theorists and practitioners. This book is ideal for university students studying in areas related to economics, management, sociology, engineering and information technology. It will be useful for both graduate students and teachers looking for reliable educational material for preparing lectures and self-study. Special attention is paid to practical aspects of statistical analysis, This book is especially relevant to ,. Who doesn’t just want to understand the theory? but also learn to apply statistical methods in real life - whether it's market analysis public opinion research or big data processing. The topics raised in the "General Theory of Statistics" deserve special attention. Here we consider the basics of probabilistic models, methods for estimating parameters, sampling theory, methods for testing hypotheses, as well as an introduction to modern directions - such as Bayesian analysis and machine learning. The author’s approach combines a strict mathematical basis with practical examples and illustrations, which helps to better assimilate the material and see its application in real-world problems. The book not only expands the horizons, but also stimulates analytical thinking, developing the ability to make informed decisions based on data. The special advantage of this textbook is its relevance. In the era of digital technology and the information revolution, statistics is becoming an integral part of any field of activity. and . and . Eliseev and M. M. Yuzbashev creates not just a theoretical basis, but a tool for understanding modern challenges and trends. Their style of presentation is lively and clear, which makes even complex concepts understandable and memorable. There are many illustrations, graphs and examples in the book, which contributes to a deeper perception of the material and stimulates interest in the subject. If you are looking for a textbook that will help you not only to master the theory of statistics, but also to develop analytical skills, to understand how to apply statistical methods in professional activities - "General theory of statistics" will be an excellent choice for you. This book is not just a textbook, but a key to understanding the modern world of data, its patterns and possibilities. It is worth having in the library anyone who appreciates accuracy, analytical approach and strives for professional growth in the field of statistics and data analysis. In general, the "General Theory of Statistics" from E. and . Eliseeva and M. M. Yuzbasheva is a book that deservedly ranks among the best textbooks on statistics . It combines academic rigor and practical focus, making it indispensable for those who want not just to learn, but to master the skills necessary for a successful career in the modern world of information and data.