How do we make sense of all the data that is being generated? Traditional statistics seems to fail to analyze such huge numbers and permutations and some mathematicians have suggested that Bayesian methods might be the answer.
Search for an explanation of Bayes Theorem or Bayes Statistics and you will find countless documents. One search yielded lots of material but the interesting one from a non-scientist. It is called “An intuitive explanation of Bayes’ Theorem” by Eliezer S Yudkowsky.
Rather than repeat all the information and a simplification, here are a few links:
http://yudkowsky.net/rational/bayes
Easier explanation of the Yudkowsky explanation is here: http://commonsenseatheism.com/?p=13156
Another book that mentions it in a very visual way is The Signal and the Noise by Nate Silver which also shows some of the traditional examples visually. The part that concerns most people about Bayes theorem is the “prior”. This has lead to most discussions in the literature since that is the “arbitrary” assignment of probability to the event and can introduce bias in the Bayes equation. However, the prior just brings out the unknown into the open. It will be interesting to see how Bayes is applicable for Biologists since most biological phenomenon have a normal distribution that is easily modeled in statistics.