Good article on the abuse of p-values: http://www.sciencenews.org/view/feature/id/57091/title/Odds_are,_its_wrong

In A Technical Explanation of Technical Explanation, Eliezer writes,

You should only assign a calibrated confidence of 98% if you're confident enough that you think you could answer a hundred similar questions, of equal difficulty, one after the other, each independent from the others, and be wrong, on average, about twice. We'll keep track of how often you're right, over time, and if it turns out that when you say "90% sure" you're right about 7 times out of 10, then we'll say you're poorly calibrated.

...

What we mean by "probability" is that if you utter the words "two percent probability" on fifty independent occasions, it better not happen more than once

...

If you say "98% probable" a thousand times, and you are surprised only five times, we still ding you for poor calibration. You're allocating too much probability mass to the possibility that you're wrong. You should say "99.5% probable" to maximize your score. The scoring rule rewards accurate calibration, encouraging neither humility nor arrogance.

So I have a question. Is this not an endorsement of frequentism? I don't think I understand fully, but isn't counting the instances of the event exactly frequentist methodology? How could this be Bayesian?

Cool paper: When Did Bayesian Inference Become “Bayesian”?

http://ba.stat.cmu.edu/journal/2006/vol01/issue01/fienberg.pdf

It means that you had a deep understanding for a few seconds, and then lost it. Or that you got trapped in the same confusion as the author, absorbed what made it seem appealing, and then "corrected away" the confusion.

To determine which one happened, try the following:

• reading it again
• rephrasing it in your own words as many different ways as you can
• seeing how the thesis connects to other topics, and if that connection can be independently verified

Eventually, you should be able to either gain the understanding, or recognize where the error is.

"If you can't explain it simply, you don't understand it well enough." Albert Einstein

This relates well to my earlier frustration about the cop-out of vaguely appealing to life experience in an argument, without actually explaining anything.

I'm a little late to this game, but I spent over an hour, maybe two, comparing the information from the two websites. I had known nothing previously about the case.

My answers: 1: 0.05; 2: 0.05; 3: 0.95; 4: 0.65

So, I feel pretty vindicated. This was a great complement to Kaj Sotala's post on Bayesianism. With his post in mind, as I was considering this case, I assigned probabilities to the existence of an orgy gone wrong as against one rape and murder from one person. There is strong Bayesian evidence for Guédé's guilt, but it's exceedingly weak for Sollecito and Knox. This has really helped the idea of Bayesianism "click" for me.

komponisto, your reasoning is wonderfully thorough and sound. I can corroborate that I deliberately found myself "shutting the voice out" concerning the activity with the mop. You have a great explanation, overall. These two posts of yours are in the running for my all-time favorites.

No joke (and I don't know about Walden Two).

The norm on this forum is to leave out any form of address from nearly all comments and replies to people's comments, so your addressing Robin as "Prof. Hanson" stuck out glaringly.

In the context of this discussion, it seemed as if you'd interpreted Robin's request as a "status transaction" and decided to respond in kind. The honorific foregrounds Robin's academic credentials and downplays the (tacitly assumed) norm that this blog is a conversation among peers, where evidence and argument are sought more than assurances of authority.

Humility, as I understand it from the Twelve Virtues pamphlet, isn't about comparing yourself to others. It is about comparing yourself to who you will become.

I am too, but this is the internet not a classroom. Call him "Robin" or "dude" or "listen man", whatever.

Prof. Hanson,

Wow, that's formal.

You should declare your age here, and whether you have ever known so much more about anything, relative to someone else, that this might have been a valid claim for you to make in a conversation.

Telling someone that they just lack enough experience to appreciate some point is, if true, extremely valuable info. If it seems reasonable to end the conversation after receiving such info, then why shouldn't the conversation end at that point? The issue isn't Bayesian vs. not, but how easily could they communicate all the specific data on which their overall judgment is based.