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Inference methods that recognize the clustering of individual observations have been available for more than 25 years. Brent Moulton (1990) caught the attention of economists when he demonstrated the serious biases that can result in estimating the effects of aggregate explanatory variables on individual-specific response variables. The source of the downward bias in the usual ordinary least-squares (OLS) standard errors is the presence of an unobserved, state-level effect in the error term. More recently, John Pepper (2002) showed how accounting for multi-level clustering can have dramatic effects on t statistics. While adjusting for clustering is much more common than it was 10 years ago, inference methods robust to cluster correlation are not used routinely across all relevant settings. In this paper, I provide an overview of applications of cluster-sample methods, both to cluster samples and to panel data sets.