This paper provides a practitioner's guide to cluster-robust inference in regression models with clustered data. It discusses the importance of using cluster-robust standard errors to account for within-cluster correlation, which can lead to biased standard errors and misleading inferences if ignored. The paper outlines the basic method of cluster-robust inference, including the use of cluster-robust standard errors, and addresses complications such as cluster-specific fixed effects, few clusters, multi-way clustering, and estimators other than OLS. It also discusses the econometric theory behind cluster-robust inference and provides practical guidance on implementing it, including the use of Stata commands. The paper highlights the importance of using cluster-robust standard errors in various applications, such as individual-level cross-section data with clustering on geographical regions and differences-in-differences studies with clustering on states. It also discusses the limitations of cluster-robust inference, particularly when there are few clusters, and provides guidance on how to handle such cases. The paper concludes with a discussion of the broader implications of cluster-robust inference for econometric practice.This paper provides a practitioner's guide to cluster-robust inference in regression models with clustered data. It discusses the importance of using cluster-robust standard errors to account for within-cluster correlation, which can lead to biased standard errors and misleading inferences if ignored. The paper outlines the basic method of cluster-robust inference, including the use of cluster-robust standard errors, and addresses complications such as cluster-specific fixed effects, few clusters, multi-way clustering, and estimators other than OLS. It also discusses the econometric theory behind cluster-robust inference and provides practical guidance on implementing it, including the use of Stata commands. The paper highlights the importance of using cluster-robust standard errors in various applications, such as individual-level cross-section data with clustering on geographical regions and differences-in-differences studies with clustering on states. It also discusses the limitations of cluster-robust inference, particularly when there are few clusters, and provides guidance on how to handle such cases. The paper concludes with a discussion of the broader implications of cluster-robust inference for econometric practice.