Introduction
In the field of statistics, uncertainty is ubiquitous, as is probability theory. However, the approach we adopt to address this uncertainty makes a major difference. In probability theory, it is often assumed that the underlying law is known with accuracy, and the main objective is to characterise the properties of the random variable that follows this law. However, in statistics, we often aim to infer information about the underlying law from observed data. In this perspective, spectral estimation plays a crucial role in enabling us to understand the frequency properties of random processes, which is essential for drawing reliable conclusions from empirical data. During this exploration, we will provide an insight into the theory of estimation, defining what an estimator is, examining its desirable qualities, and addressing specific techniques such as moment estimation and spectral density estimation methods.