Nonparametric statistics are
statistics not based on
parameterized families of
probability distributions. They include both
descriptive and
inferential statistics. The typical parameters are the mean, variance, etc. Unlike
parametric statistics, nonparametric statistics make no assumptions about the
probability distributions of the variables being assessed. The difference between
parametric models and
non-parametric models is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data. Note that the
non-parametric model does not have no parameters: parameters are determined by the training data, not the model.