This chapter introduces the core logic of statistical inference, focusing on how we use sample data to make probabilistic statements about populations. We begin with the hypothesis testing framework, including null and alternative hypotheses, test statistics, and p-values. Key concepts such as confidence intervals and type I/II errors are explained in depth. Specific methods for one-sample and two-sample inferences are discussed, along with inference for categorical data. We also introduce divergence metrics, which provide alternative ways to compare distributions—a concept increasingly important in modern data science applications.