So far, we have presented how noise, bias and variance can lower the true or perceived performance of a model. An unsatisfactory model performance is detected easily, but the contributions of various error sources are often hardly distinguishable. We hope that the tools and insights presented above can aid scientists to better understand the sources of error in their models. This understanding can guide the next steps to optimize the model. Another possible pitfall comes when the performance of a model on the initial test set is much better than its performance on the actual molecules of interest, which can be hard to detect. In fact, models that reportedly perform well but fail in real-world applications are a major concern and setback within the machine learning community. In the following, we discuss two important reasons a model may seem to perform deceptively better than it actually does.