In this chapter, we examine how to quantify uncertainty about model parameters, highlighting two main approaches: frequentist and Bayesian. We start by modelling a data-generating mechanism with a parametric family, where different parameter values correspond to different models. Assuming our model family can describe the mechanism, we use data to infer plausible parameters and quantify uncertainty. In frequentist inference, we build parameter estimators and study their sampling distributions across repeated data collection. Here, parameters are fixed unknown constants, and only estimators are treated probabilistically. In Bayesian inference, parameters are latent random variables. We express uncertainty through probability, combining prior beliefs about parameter values with observed data using Bayes’ rule to obtain a posterior distribution. The posterior and the frequentist sampling distribution often play similar roles and can resemble each other in practice. Computational tools like bootstrapping and Markov chain Monte Carlo help estimate sampling and posterior distributions, respectively.