This chapter introduces the characteristics of water resources data and discusses various measures of location, spread, and skewness. It emphasizes the importance of robust and resistant techniques, such as the median and interquartile range, which are less affected by outliers compared to classical measures like the mean and standard deviation. The chapter also covers transformations, including the logarithmic transformation, which can make data more symmetric and suitable for certain statistical analyses. Additionally, it highlights the significance of graphical methods in data analysis, such as histograms, stem and leaf diagrams, quantile plots, and boxplots, which provide visual summaries and insights into the data. The chapter concludes with a discussion on the construction of these graphical representations and their applications in exploratory data analysis (EDA).This chapter introduces the characteristics of water resources data and discusses various measures of location, spread, and skewness. It emphasizes the importance of robust and resistant techniques, such as the median and interquartile range, which are less affected by outliers compared to classical measures like the mean and standard deviation. The chapter also covers transformations, including the logarithmic transformation, which can make data more symmetric and suitable for certain statistical analyses. Additionally, it highlights the significance of graphical methods in data analysis, such as histograms, stem and leaf diagrams, quantile plots, and boxplots, which provide visual summaries and insights into the data. The chapter concludes with a discussion on the construction of these graphical representations and their applications in exploratory data analysis (EDA).