Our goal was to investigate the basis of human plausibility judgements. Previous research had suggested that plausibility is affected by two factors: concept coherence (the inferences made between parts of a discourse) and word coherence (the distributional properties of the words used). In two experiments, participants were asked to rate the plausibility of sentence pairs describing events. In the first, we manipulated concept coherence by using different inference types to link the sentences in a pair (e.g., causal or temporal). In the second, we manipulated word coherence by using latent semantic analysis, so two sentence pairs describing the same event had different distributional properties. The results showed that inference type affects plausibility; sentence pairs linked by causal inferences were rated highest, followed by attributal, temporal, and unrelated inferences. The distributional manipulations had no reliable effect on plausibility ratings. We conclude that the processes involved in rating plausibility are based on evaluating concept coherence, not word coherence.