This chapter discusses confidence intervals. We begin by exploring confidence intervals for the mean, particularly in cases where the sample data follow a normal distribution. With some practical examples, I show how to calculate them, providing a way of assessing how sample averages are compared to the true population mean. The chapter then introduces the central limit theorem, which allows us to approximate confidence intervals even when the distribution is not normal, particularly in the case of large sample sizes. For situations where the normality assumption does not hold, we discuss briefly the bootstrap method. This powerful, non-parametric technique is useful when traditional methods fail, and it offers a flexible way to estimate confidence intervals without requiring prior knowledge of the underlying distribution.