In the following, we describe the influence of noise, bias and variance on the observed model performance, as well as possible pitfalls associated with each type of error. We often discuss the shape of the learning curve, i.e. the test set error as it depends on the size of a dataset, as different types of limitations caused by noise, bias or variance can lead to unique patterns in the learning curve. The slope of the learning curve characterizes the change in error upon addition of data and can be utilized to predict how much data is needed to achieve a specific accuracy. In general, a steep, negative slope on a log-log plot without plateaus is desirable.