In a recent paper, Beall, Hofer, and Schaller (2016) used observational time series data to test the hypothesis that the 2014 Ebola outbreak influenced the 2014 U.S. Federal Elections. They found substantial associations between online search volume for Ebola and people’s tendency to vote Republican, an effect observed primarily in states with norms favoring Republican candidates. However, the analyses did not deal with the well-known problem of temporal autocorrelation in time series. We show that all variables analyzed exhibit extremely high levels of temporal autocorrelation (i.e. similarity in data-point values across time). After removing first-order autocorrelation, the observed relationships are attenuated and non-significant, indicating that current data do not provide sufficient evidence for a relationship between the 2014 Ebola outbreak and the 2014 U.S. Federal Elections. We conclude by highlighting other pitfalls of observational data analysis, and draw attention to analytical strategies developed in related disciplines for avoiding these errors.