This chapter discusses the importance of accurate statistical inference in regression models when data are grouped into clusters, where errors are uncorrelated across clusters but correlated within clusters. The authors, Colin Cameron and Douglas L. Miller, emphasize that default standard errors can overstate the precision of estimator estimates, leading to misleadingly narrow confidence intervals and over-rejection of true null hypotheses. They introduce cluster-robust standard errors as a solution, which do not require specification of a model for within-cluster error correlation but assume a large number of clusters. The chapter covers the basic method, its implementation in econometric software like Stata, and various complications such as cluster-specific fixed effects, few clusters, multi-way clustering, and estimators other than OLS. It also discusses the efficiency gains of feasible generalized least squares (FGLS) when errors are correlated within clusters and provides practical guidance on how to implement cluster-robust inference in different scenarios.This chapter discusses the importance of accurate statistical inference in regression models when data are grouped into clusters, where errors are uncorrelated across clusters but correlated within clusters. The authors, Colin Cameron and Douglas L. Miller, emphasize that default standard errors can overstate the precision of estimator estimates, leading to misleadingly narrow confidence intervals and over-rejection of true null hypotheses. They introduce cluster-robust standard errors as a solution, which do not require specification of a model for within-cluster error correlation but assume a large number of clusters. The chapter covers the basic method, its implementation in econometric software like Stata, and various complications such as cluster-specific fixed effects, few clusters, multi-way clustering, and estimators other than OLS. It also discusses the efficiency gains of feasible generalized least squares (FGLS) when errors are correlated within clusters and provides practical guidance on how to implement cluster-robust inference in different scenarios.