This chapter covers parameter estimation, a fundamental aspect of inductive statistics. It allows generalizations to be made about a population based on sample data. The chapter covers two main estimation procedures: point estimation, which provides a single value estimate for population parameters, and interval estimation, which provides an interval within which the parameter is likely to lie. Through illustrative examples and theoretical explanations, the concepts of sampling distributions, standard error, and confidence intervals are explained, emphasizing the importance of sample size and the central limit theorem in ensuring accurate and reliable estimates.