tag:blogger.com,1999:blog-8666091.post113535016318584341..comments2021-04-15T06:42:27.872+02:00Comments on The Reference Frame: E=mc2: a test ... interplay between theory and experimentLuboš Motlhttp://www.blogger.com/profile/17487263983247488359noreply@blogger.comBlogger1125tag:blogger.com,1999:blog-8666091.post-1135540001215430512005-12-25T20:46:00.000+01:002005-12-25T20:46:00.000+01:00You said:What I want to say is that there must exi...You said:<BR/><BR/><I>What I want to say is that there must exist separately conclusions derived from the experiments; and conclusions derived just from the theory. And these two sets of conclusions must be compared. If someone is showing an agreement simultaneously by twisting and cherry-picking the data according to the theory and fudging the theory according to the data, merely to show that there is a roughly consistent picture, then it is no confirmation of “the” theory. In fact, there is no particular theory, just a union of ill-defined emotions whose details can be changed at any time. It’s not science and one cannot expect a "theory" obtained in this way to have any predictive power. This is how the priests in 15th century argued that the real world is consistent with the Bible.</I><BR/><BR/>I agree with this comment insofar as one should not try in an <I>ad hoc</I> way to fudge theory and to twist experiment so that they agree.<BR/><BR/>However, the <I>Bayesian</I> approach is a rigorous and consistent way of matching up theory and data. When you look at what Bayes gets up to when you relate a number of alternative models (or a <I>continuuum</I> of alternatives) to the data (assumed noisy and incomplete, as usual), then in effect Bayes is doing all of this twisting and fudging that you don't like.<BR/><BR/>Bayes considers <I>all</I> of the alternative models that you have told it about, and it tells you how to compute a probability for each of these alternatives. The structure of the expression for this probability is where you find the "twisting and fudging" going on. For instance, with a continuum of models that differ only in the value of a fundamental constant, the Bayesian approach tells you how to compute a "posterior" probability over the values of the constant, and the structure of this expression tells you how much you have to "twist and fudge" to make theory and data fit each other.<BR/><BR/>As long as you "twist and fudge" rigorously and consistently (i.e. use Bayes) then it is a scientifically respectable activity!Legacy Userhttps://www.blogger.com/profile/16386879829062354101noreply@blogger.com