A second misuse occurs where the performance of a fitted model or classification rule is reported as a raw result, without allowing for the effects of model-selection and the tuning of parameters based on the data being analyzed.
At its most simple, it can include the decision to remove outliers, after noticing this might help improve the analysis of an experiment.
In functional magnetic resonance imaging (fMRI) data, for example, considerable amounts of pre-processing is often needed.
Similarly, the classifiers used in a multivoxel pattern analysis of fMRI data require parameters, which could be tuned to maximise the classification accuracy.
This is a standard technique[citation needed] used (for example) by the princeton MVPA classification library.