Beyond the basics (mean, variance, distributions, simple test, regression, ...) statistics looks like a huge mess -to me- .(adsbygoogle = window.adsbygoogle || []).push({});

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.

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# Statistics looks so difficult, no way to simplify ?

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