parametric method assumes more, if the assumption is true, more inference can be done. If the assumption (specification) is wrong, then it makes less sense. But, there are quite a few misspcification tests such that one can check for the correctness the assumption made.

with nonparametric approach, one assumes less such that he would never make any mistake. The point is that less inference from the results can be done than with parametric methods.

It may difficult to use two line to give you a 简单的例子. try to read some stuff on kernel density estimation, ..Nadaraya-Waston (NW) estimation. You aslo need to be very good in statistical inference for you to better understand the difference here, at least I think.

谢谢，我大概知道了两者的优缺点。（根据你的意思，第二段最后一句中的parametric好像应该是nonparametric）

不过如果有哪位大哥能举个例子说明用法上的不同就好了。

根据你的意思，第二段最后一句中的parametric好像应该是nonparametric

No^, I am correct: ...than....