Hi Guys,

This is just a copypasta of a post I made to **Kaggle.com/fourms**. It got no responses in 6 days! Maybe it was just a bad question? Anyway, any material you can recommend would be appreciated...

First post, so please be gentle. Also, keep in mind I only went for a math minor (finance major).

OK, so up until last week I had no idea non-parametric statistics even existed. As far as my preliminary research has indicated, unlike parametric statistics, non-parametric statistics do not use distributions to make inference about a total population from a sample (and also predominately use medians in lieu of means).

After taking abstract algebra and some discrete mathematics, I am fairly competent with set theory and cantor notation. I also took an advanced logic class covering methods of proof ("reductio ad absurdum" and all that).

All the texts I have found refer to "assumptions" that must be true in order to apply parametric statistics to a sample, but they don't go over what these are or how to test for them!

Can you guys recommend a book(s) that...

- Enumerates the assumptions made by parametric models (Gauss, F, T, Z, etc), describes how to test for these with set theory (preferably cantor notation), and does a good job of delineating these proofs.
- A book which acts as a road-map for different statistical techniques, which assumes absolute familiarity with basic parametric statistics. What I have found so far has been so rudimentary, I spend all my time re-reading verbose descriptions of basic stat.
Thanks Guys!

Perhaps incorrectly, I am basing much of my current understanding off these two finds during my googling:

23 1 Parametric vs non parametric statistics 10 22

Choosing Between a Nonparametric Test and a Parametric Test

Any input would be appreciated!

Cheers,

TJ