This document introduces folks to survey weighted analysis in R using the survey package. Through a series of examples, we compare unweighted estimates, simple weighted estimates, and design-based survey estimates to show how weighting affects means, subgroup comparisons, and regression results. Using real survey variables (age, education, and party identification), the document highlights where weights matter most, how they influence uncertainty, and why design based methods are essential for valid population inference. The goal is to build intuition for when and why survey weights should be used, not just how to implement them in code.