How do you justify your conclusion in the case of Russia, for example, where the government prohibits ownership of firearms; yet, as of the last year or two, Russia leads the world in the number of homicides and/or deaths by gunfire.

Or take a different extreme view. Cuba provides a good example. In that island, the government has complete control over every aspect of human life. Predictably, Cuba has a very low incidence of homicides and/or death by gunfire (with the exclusion of suicides, where, as of recent, it was among the world’s leaders). In this case, the “price to be paid” is the removal of personal freedoms, the absence of civil rights or due process, and the indifference of the government towards self dignity and individual enterprise.

The better question is the perennial, “what gives”? Of the factors in your study, the one that makes the most sense to me is “level of education”. If nothing else, perhaps it can be inferred that our national system of education appears to be failing the nation.

Regards,

Frank P. Soler, Ph.D.
Professor Emeritus
Math/Stats Dept.
De Ana College
Cupertino, CA 95014

Lesser, L. M. & Glickman, Mark E. (2009). Using Magic in the Teaching of Probability and Statistics. Model Assisted Statistics and Applications, 4(4), 265-274.

MAYO: But they should use existing knowledge.

COX: Knowledge yes. Prior knowledge will go into constructing the model in the first place or even asking the question or even finding it at all interesting. It’s not evidence that should be used if let’s say a group of surgeons claim we are very, very strongly convinced, maybe to probability 0.99, that this surgical procedure works and is good for patients, without inquiring where the 0.99 came from. It’s a very dangerous line of argument. But not unknown.

When Cox says in our conversation:
“In fact you have very clever ways of making sure that your analysis is valid even if the prior information is totally wrong. If you use the wrong prior information you just got an inefficient design, that’s all” (p. 105),
he is using “prior information” to refer to various uncorroborated beliefs, conjectures, hunches, whether or not they are influenced by policy, ethical, or other values, and for which no evidence is given. That is why he mentions the possibility that “the prior information is totally wrong”.

Ironically, it is Gelman who declares in this article that “A Bayesian wants everybody else to be a non-Bayesian”:
“No funny stuff, no posterior distributions, just the likelihood. . . . I don’t want everybody coming to me with their posterior distribution—I’d just have to divide away their prior distributions before getting to my own analysis.”
I discuss this in a series of posts on my blog beginning http://errorstatistics.com/2012/09/12/u-phil-how-should-prior-information-enter-in-statistical-inference/

Our exchange may be found here: http://www.rmm-journal.de/downloads/Article_Cox_Mayo.pdf

If solely there were a lot more websites such as this excellent one on
the word wide web. Well, thanks for your time, Coy