[1] Conversely nonparametric statistics does not assume explicit (finite-parametric) mathematical forms for distributions when modeling data.
[2] Regarding nonparametric (and semiparametric) models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".
[3] The normal family of distributions all have the same general shape and are parameterized by mean and standard deviation.
Suppose that we have a sample of 99 test scores with a mean of 100 and a standard deviation of 1.
Parametric statistical methods are used to compute the 2.33 value above, given 99 independent observations from the same normal distribution.