A trial in the context of Hyperopt is a single evaluation of the objective function with a specific set of hyperparameters. The outcome of a trial, typically the performance metric such as accuracy or loss, is used to inform the optimization algorithm about which regions of the hyperparameter space are promising. This feedback loop helps the algorithm to make more informed decisions in subsequent trials, guiding the search towards hyperparameter combinations that are likely to yield better performance.