A problem in event history analysis is that time is measured imprecisely. Events are typically known to occur within discrete time units (e.g. day, month or year). Discrete measurement of the start and end time of an event leads to a known interval within which the event duration falls. The event duration is interval censored. When ignored, interval censoring is shown to introduce considerable bias to parameter estimates and heighten the risk of inference errors. I show that treating the

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... n as an interval reduces bias and improves the performance of hypothesis tests. Replications of analyses from four political science articles in leading journals demonstrate that substantive inferences depend on the use of appropriate methods for interval censored duration data.