In deep learning, the phase where a trained model, such as $\hat{\mathbf{w}}^ op \mathbf{x} + \hat{b}$, is used to estimate outputs for new, unseen examples is often referred to as inference. However, this usage is a misnomer that can cause confusion with the statistics literature, where inference more broadly denotes any conclusion drawn from evidence, particularly parameter inference. To avoid this ambiguity, it is more precise to use the term prediction for the process of estimating labels for new instances.