In this article, we highlight three points. First, we counter Grant and Lebo's claim that the error correction model (ECM) cannot be applied to stationary data. We maintain that when data are properly stationary, the ECM is an entirely appropriate model. We clarify that for a model to be properly stationary, it must be balanced. Second, we contend that while fractional integration techniques can be useful, they also have important weaknesses, especially when applied to many time series typical in political science. We also highlight two related but often ignored complications in time series: low power and overfitting. We argue that the statistical tests used in time-series analyses have little power to detect differences in many of the sample sizes typical in political science. Moreover, given the small sample sizes, many analysts overfit their time-series models. Overfitting occurs when a statical model describes random error or noise instead of the underlying relationship. We argue that the results in the Grant and Lebo replications could easily be a function of overfitting.