Statistics looks so difficult, no way to simplify ?

[lalbatros]

Beyond the basics (mean, variance, distributions, simple test, regression, ...) statistics looks like a huge mess -to me- .

Any method has typically 10 variants and can be 'colored' in 10 flavors,
not to mention technicalities like missing data handling, binary data, ... and people's names
I need to think a long time before I understand the main point of a method (say the bartlett test in a PCA).
I never feel confident for "the details and the options".
Many things never get translated in simple words.
Mathematical descriptions are not (easily) available or are (practically) unreadable.

Sometimes I have the feeling that a "huge monte carlo" point of view could help to simplify the understanding.
For example, a statistical test could be implemented by a "monte carlo" method. If computers had a huge computing power, recourse to specific tests (hard to develop, get and understand) would be useless.
At least, this could be a language to express a "statistical problem".

Could some of you give me some related ideas, papers or web sites?
I am equally interrested by nicely-written material, organized references, unification attempts.
I would like to see the forest, not the trees.

 

[EnumaElish]

http://en.wikipedia.org/wiki/Monte_Carlo_method#References