I'm reading PDQ Statistics, I have a small base of statistics and I can't figure out this statement from the book.
They assure in the book that
It seems that most people who have taken a stats course forget this basic idea when they start to worry about when to use parametric statistics such as t tests. Although it is true that parametric statistics hang on the idea of a normal distribution, all we need is a normal distribution of the means, not of the original data.
Second point I find this sentence a bit fuzzy
The basic difference is that, in parametric statistics, the DV (note: dependent variable) is some measured quantity (a ratio or interval level variable), so it makes sense to calculate means and SDs. With nonparametric stats, the DV is usually either a count or ranking (such as of dead bodies, which happen often, or cures, which happen rarely) for which it makes no sense to calculate means (eg,“The average religion of Americans is 2.67”).
I always thought as also stated here that:
A parametric test is a test in which you assume as working hypothesis an underlying distribution for your data, while a non-parametric test is a test done without assuming any particular distribution.
Is the book a true simplification or it's totally wrong? Am I not catching something?
: Norman, G.R. and D.L. Streiner (2003)
PDQ Statistics, Volume 1, 3rd Ed,
People's Medical Publishing House, Shelton, CT