LAUSR

Welcome to the first Political Analysis replication forum. This was not a planned event, but something that grew organically, and by necessity, over the last 3 years. In 2016 Political Analysis published an important and influential article by Muchlinski, Siroky, He, and Kocher advocating random forest models over conventional logit regression to predict the onset of civil war. Subsequently in 2017 two separate and completely independent Letters (Neunhoeffer and Sternberg 2018; Wang 2018) were submitted after finding the technical issues of estimation and prediction discussed in this forum. These manuscripts were reviewed and are contained herein. This also commenced a detailed replication and review process within the editorial team at Political Analysis that producedanadditional Letter (Heuberger 2018) focusingon technical details of the code and the process. Finally, after much discussion internally and externally we decided that the best outcome would be a positive jointly produced set of Letters in a forum that not only discusses the original analysis of Muchlinski et al. (2016), but also the difficulties in replicating complex, sophisticated work in the current era. We add to this chronology another letter received later in the process that independently discusses issues, challenges, and prescriptive advice in replication. JeffreyHardin, AnandSokhey, andHannahWilsondevelop a framework for improving replication and apply it to a preregistered replication study. Our hope is that readers will not only enjoy reading about the issues, and chronology in theseworks, butwill also fully appreciate some challenges we face in evaluating the work of our peers in political methodology. In my view the most important takeaway from the process that created this forum is the value of the Political Analysis replication process put in place by my predecessors R. Michael Alvarez and Jonathan Katz. What it means is that we as a subfield evaluate and quality-control ourselves, catching issues that need to be addressed so that scholars can use the knowledge, methods, and approaches published in Political Analysis with confidence that they are not just vetted at review time but also throughout the life of the article. I know of no other subfield of political science that is as intensively self-critical and self-reflective in this way. I hope this forum provides readers of Political Analysiswith anassurance thatwe value the reliability of published findings at thehighest possible levels. In 1995 Gary King wrote: “As virtually every good methodology text explains, the only way to understand and evaluate an empirical analysis fully is to know the exact process by which the data were generatedand theanalysis produced” (italics in the original). This remains true today, but not uniformly appreciated 23 years later (italics are mine here). The community of empirical political scientists has benefited immeasurably by the recent trend in required replication that is now standard at leading journals of the field, and started here at Political Analysis. I am the last person to have “natural science envy” as I also pay attention to the big challenges in data analysis in other fields, but the standard for acceptance of results inmany of these fields is replication where failed replication (or unavailable replication) constitutes nonbelievability. Consider the episode in 2011 where researchers at CERN in Switzerland appeared to have measured a neutrino traveling at faster than the speed of light. They, not having full belief in their own findings even after inspecting themclosely, immediately released the data to all of the physicsworld for confirmation or refutation. Predictably the results failed to be supported, and this was due to a reason that