Statistics looks so difficult, no way to simplify ?

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SUMMARY

The discussion centers on the complexities of advanced statistics, particularly the challenges in understanding various methods such as the Bartlett test in Principal Component Analysis (PCA). The participant expresses frustration with the multitude of variants and technicalities in statistical methods, including missing data handling and binary data. They propose that a Monte Carlo approach could simplify statistical understanding by leveraging computational power to replace complex tests. The participant seeks resources, papers, and well-organized references to aid in grasping these concepts more clearly.

PREREQUISITES
  • Understanding of basic statistical concepts such as mean, variance, and distributions.
  • Familiarity with Principal Component Analysis (PCA) and the Bartlett test.
  • Knowledge of Monte Carlo methods and their applications in statistics.
  • Ability to interpret statistical literature and technical documentation.
NEXT STEPS
  • Research Monte Carlo methods in statistics and their practical applications.
  • Explore advanced statistical tests and their variants, focusing on the Bartlett test.
  • Investigate resources that simplify complex statistical concepts, such as online courses or textbooks.
  • Look for organized references and papers that unify various statistical methods and their interpretations.
USEFUL FOR

Statisticians, data analysts, researchers, and students seeking to deepen their understanding of advanced statistical methods and simplify complex concepts through computational approaches.

lalbatros
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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.
 
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