Statistical analysis is a cornerstone in civil engineering, providing essential tools for interpreting data and making informed decisions in the face of uncertainty. This chapter explores the vast statistical capabilities of MATLAB, focusing on both foundational and advanced techniques that empower civil engineers to analyze data with precision. The chapter begins with essential concepts such as descriptive statistics, where measures of central tendency and variability—mean, median, standard deviation, and variance—are computed to summarize datasets. MATLAB’s ability to handle large-scale data through built-in functions such as mean(), std(), and var() streamlines these processes. As the chapter progresses, more complex statistical methods, including correlation, regression analysis, and hypothesis testing, are introduced. These tools are essential for modeling relationships between variables, evaluating material properties, and conducting risk assessments. The Statistics and Machine Learning Toolbox expands MATLAB’s capabilities, further offering robust methods for multivariate statistics, regression models, and data fitting techniques. Real-world examples such as regression modeling of material strength and reliability analysis of structures highlight MATLAB’s effectiveness in solving critical civil engineering problems. By mastering the statistical functions outlined in this chapter, civil engineers are equipped to process and interpret data with greater confidence, optimize designs, and enhance decision-making processes based on probabilistic insights. This chapter sets the stage for applying statistical rigor to complex engineering tasks, ensuring that designs and analyses are grounded in sound, data-driven conclusions.