**Summary:** This book, *Techniques of Water-Resources Investigations of the United States Geological Survey*, provides a comprehensive guide to statistical methods used in water resource analysis. It is part of a series of USGS publications and has been superseded by newer techniques and methods. The book is structured into 15 chapters, covering topics such as data summarization, graphical analysis, confidence intervals, hypothesis testing, regression, and other statistical techniques relevant to water resources. Chapter 1 introduces the characteristics of water resources data, including non-normal distributions, skewness, and censored data. It discusses measures of location, such as the mean and median, and their respective strengths and weaknesses. The median is highlighted as a more resistant measure to outliers compared to the mean. Chapter 2 focuses on graphical data analysis, including histograms, boxplots, probability plots, and scatterplots. These tools are essential for visualizing data patterns and understanding distributions. The chapter emphasizes the importance of graphical methods in exploratory data analysis (EDA) and their role in identifying outliers and trends. Chapters 3 through 16 delve into various statistical techniques, including confidence intervals, hypothesis tests, regression analysis, and methods for handling censored data. The book emphasizes the use of robust and nonparametric methods due to the nature of water resources data, which often exhibit skewness, outliers, and non-normality. The text also covers advanced topics such as multiple regression, trend analysis, and discrete relationships. It provides detailed explanations of statistical tests, their assumptions, and how to interpret results. The book includes practical examples and exercises to help readers apply the methods discussed. The final chapters discuss the importance of graphical presentation in data analysis, the use of transformations to improve data symmetry and linearity, and the selection of appropriate statistical techniques based on data characteristics. The book concludes with a reference list, appendices, and an index for easy navigation. Overall, this book serves as a valuable resource for hydrologists and water resources scientists, offering a thorough understanding of statistical methods tailored to the unique challenges of water resource data analysis.**Summary:** This book, *Techniques of Water-Resources Investigations of the United States Geological Survey*, provides a comprehensive guide to statistical methods used in water resource analysis. It is part of a series of USGS publications and has been superseded by newer techniques and methods. The book is structured into 15 chapters, covering topics such as data summarization, graphical analysis, confidence intervals, hypothesis testing, regression, and other statistical techniques relevant to water resources. Chapter 1 introduces the characteristics of water resources data, including non-normal distributions, skewness, and censored data. It discusses measures of location, such as the mean and median, and their respective strengths and weaknesses. The median is highlighted as a more resistant measure to outliers compared to the mean. Chapter 2 focuses on graphical data analysis, including histograms, boxplots, probability plots, and scatterplots. These tools are essential for visualizing data patterns and understanding distributions. The chapter emphasizes the importance of graphical methods in exploratory data analysis (EDA) and their role in identifying outliers and trends. Chapters 3 through 16 delve into various statistical techniques, including confidence intervals, hypothesis tests, regression analysis, and methods for handling censored data. The book emphasizes the use of robust and nonparametric methods due to the nature of water resources data, which often exhibit skewness, outliers, and non-normality. The text also covers advanced topics such as multiple regression, trend analysis, and discrete relationships. It provides detailed explanations of statistical tests, their assumptions, and how to interpret results. The book includes practical examples and exercises to help readers apply the methods discussed. The final chapters discuss the importance of graphical presentation in data analysis, the use of transformations to improve data symmetry and linearity, and the selection of appropriate statistical techniques based on data characteristics. The book concludes with a reference list, appendices, and an index for easy navigation. Overall, this book serves as a valuable resource for hydrologists and water resources scientists, offering a thorough understanding of statistical methods tailored to the unique challenges of water resource data analysis.