Although it seems intuitive to imagine that a very large amount of data improves the performance of a model, it may be that too much data can make analysis more complicated. It can therefore sometimes be worthwhile to reduce the quantity or size of data, in order to improve storage capacities and reduce analysis costs, without losing performance (or, in some cases, even gaining it). There are a number of data reduction techniques, for example, we can choose a certain number of variables that we prefer to keep and drop others. The choice of relevant variables can be made by analysis of the variable's p-value, or by decision tree techniques which give us an estimate of the importance of the various descriptors.