Hi Wirt, I completely agree with almost all of what you (and David) wrote. Feynman is talking about a real hypothesis that arose from a great deal of thought and creativity...not one that has been attached with baling wire, duct tape and a little leftover Juicy Fruit to a pile of data that happened to be sitting around. That said, science is many things - 'a predictive enterprise, not some form of mindless after-the-fact exercise in number crunching.' - fits under the umbrella but I don't think captures the whole enterprise. Sequencing the human genome was, in my opinion, a version of mindless number crunching (although perhaps somebody can put that effort in a hypothesis testing context that I haven't thought of). I think most people would be hard pressed to say it wasn't science. In fact, there is an emerging field of statistics (data mining) that seems to be useful in developing scientific hypotheses and is all about the 'mindless after-the-fact exercise in number crunching'. My feeling is that data can provide hypotheses or test them. When it does the first, it is a very useful part of science but it is not predictive and it does not test hypotheses (null, competing or otherwise). When it does the latter it falls ito the category that Feynman was describing. I think the reason we often get these trivial tests of hypotheses is because there is this sense that science is only about testing hypotheses - therefore to do science I must test a hypothesis...whether there is a meaningful one or not. In my opinion, science can also just be about looking for patterns that we can use to suggest hypotheses. Hypotheses have to be tested to be useful but the patterns we see in nature (and those patterns are often less distinct without number crunching)are almost always the birthplace of hypotheses. Best.
Jeff H -----Original Message----- From: Wirt Atmar <[EMAIL PROTECTED]> To: [email protected] Date: Wed, 20 Feb 2008 12:03:54 -0700 Subject: [ECOLOG-L] Anderson's new book, "Model Based Inference in the Life Sciences" I just purchased David Anderson's new book, "Model Based Inference in the Life Sciences: a primer on evidence," and although I've only had the opportunity to read just the first two chapters, I wanted to write and express my enthusiasm for both the book and especially its first chapter. David and Ken Burnham once bought me lunch, and because my loyalties are easily purchased, I may be somewhat biased in my approach towards the book, but David writes something very important in the first chapter that I have been mildly railing against for sometime now too: the uncritical overuse of null hypotheses in ecology. Indeed, I believe this to be such an important topic that I wish he had extended the section for several more pages. What he does write is this, in part: "It is important to realize that null hypothesis testing was *not* what Chamberlin wanted or advocated. We so often conclude, essentially, 'We rejected the null hypothesis that was uninteresting or implausible in the first place, P < 0.05.' Chamberlin wanted an *array* of *plausible* hypotheses derived and subjected to careful evaluation. We often fail to fault the trivial null hypotheses so often published in scientific journals. In most cases, the null hypothesis is hardly plausible and this makes the study vacuous from the outset... "C.R. Rao (2004), the famous Indian statistician, recently said it well, '...in current practice of testing a null hypothesis, we are asking the wrong question and getting a confusing answer'" (2008, pp. 11-12). This is so completely different than the extraordinarily successful approach that has been adopted by physics. In ecology, an experiment is most normally designed so its results may be statistically tested against a null hypothesis. In this procedure, data analysis is primarily a posteriori process, but this is an intrinsically weak test philosophically. In the end, you rarely understand more about the processes in force than you did before you began. But the analyses characteristic of physics don’t work that way. In 1964, Richard Feynman, in a lecture to students at Cornell that's available on YouTube, explained the standard procedure that has been adopted by experimental physics in this manner: "How would we look for a new law? In general we look for a new law by the following process. First, we guess it. (laughter) Then we... Don't laugh. That's the damned truth. Then we compute the consequences of the guess... to see if this is right, to see if this law we guessed is right, to see what it would imply. And then we compare those computation results to nature. Or we say to compare it to experiment, or to experience. Compare it directly with observations to see if it works. "If it disagrees with experiment, it's wrong. In that simple statement is the key to science. It doesn't make a difference how beautiful your guess is. It doesn't make a difference how smart you are, who made the guess or what his name is... (laughter) If it disagrees with experiment, it's wrong. That's all there is to it." -- http://www.youtube.com/watch?v=ozF5Cwbt6RY In physics, the model comes first, not afterwards, and that small difference underlies the whole of the success that physics has had in explaining the mechanics of the world that surrounds us. The entire array of plausible hypotheses that were advocated by Chamberlin don't all have to present during the first experimental attempt at verification of the first hypothesis; they can occur sequentially over a period of years. As David continues, "We must encourage and reward hard thinking. There must be a premium on thinking, innovation, synthesis and creativity" (p. 12), and this hard thinking must be done in advance of the experiment. Science is a predictive enterprise, not some form of mindless after-the-fact exercise in number crunching. Although expressed in a different format, David Anderson is saying the same thing as Richard Feynman, and I very much congratulate him for it. Wirt Atmar
