This article attempts to describe an approach to statistical data analysis which is simultaneously parametric and nonparametric. Given a random sample X1, …, Xn of a random variable X, one would like (1) to test the parametric goodness-of-fit hypothesis H0 that the true distribution function F is of the form F(x) = F0[(x − μ)/σ)], where F0 is specified, and (2) when H0 is not accepted, to estimate nonparametrically the true density-quantile function fQ(u) and score function J(u) = − (fQ)'(u). The article also introduces density-quantile functions, autoregressive density estimation, estimation of location and scale parameters by regression analysis of the sample quantile function, and quantile-box plots.