It is an interesting thought experiment that can be dissected in several ways depending on how it is addressed.
This article is not about setting up a proxy on your browser, skype or torrents. This is easy! Just go on your application's options and setup your proxy. You should of course have access to a proxy server that ideally does not retain any log files. If they do, then if those log files can be accessed by someone then your traffic history would be available to them. So, you got your proxy server (paid or free) with no log files and you have its port and authentication (if any). You set everything up on your browser or torrent application and that's it, right? All your traffic parsed through the proxy, others see the proxy's IP address while you are hidden behind the proxy. Well, that is not always the case and the reason boils down to he programming of the application that you are using. This is a guide on how to put an additional layer of protection through applying firewall.
It can be quite frustrating adding Wikipedia Dumps in a local database. For some Wikipedias, such as the English Wikipedia, it takes a long time. This is a collection of scripts I've used to import Wikipedia dumps in Mysql.
Note: This guide is based on an Ubuntu server setup
Warning: The restoring process is likely to take weeks for large Wikipedias such as the English Wikipedia
Contrary to NHST where you have a p value along with the effect size to determine whether there is an effect and how big is it, Bayes factor answers both of these questions. In other words, one simple result determines which hypothesis is asserted and by how much according to your data.
You determine which hypothesis is more likely given the data based on the Bayes Factor. The way to interpret a general Bayes Factor is the following. If a Bayes factor is denoted as BFxy then you say that the data are n times more likely under Hx than Hy. An example based on the BF10 would be that the data would be 0.53 times more likely under H1 than H0. If we use the BF01 then we would say that the data are 1.87 times more likely under H0 than H1(which is more meaningful). Basically, one version of Bayes factor(e.g. BF01) is the inverted version of the other(e.g BF10). Pick the version of Bayes factor that is above 1.