While conflict event data sets are increasingly used in contemporary conflict research, important concerns persist regarding the quality of the collected data. Such concerns are not necessarily new. Yet, because the methodological debate and evidence on potential errors remains scattered across different subdisciplines of social sciences, there is little consensus concerning proper reporting practices in codebooks, how best to deal with the different types of errors, and which types of errors should be prioritised. In this article, we introduce a new analytical framework—that is, the Total Event Error (TEE) framework—which aims to elucidate the methodological challenges and errors that may affect whether and how events are entered into conflict event data sets, drawing on different fields of study. Potential errors are diverse and may range from errors arising from the rationale of the media source (e.g., selection of certain types of events into the news) to errors occurring during the data collection process or the analysis phase. Based on the TEE framework, we propose a set of strategies to mitigate errors associated with the construction and use of conflict event data sets. We also identify a number of important avenues for future research concerning the methodology of creating conflict event data sets.