This thread is for discussing Massimo Pigliucci's Rationally Speaking article entitled Mr. Bayes and the true nature of scientific hypotheses.Quote:N. 20, January 2002Mr. Bayes and the true nature of scientific hypothesesHow does science work, really? You can read all about it in plenty of texts in philosophy of science, but if you have ever experienced the making of science on an everyday basis, chances are you will feel dissatisfied with the airtight account given by philosophers. Too neat, not enough mess.To be sure, I am not denying the existence of the scientific method(s), as radical philosopher Paul Feyerabend is infamously known for having done. But I know from personal experience that scientists don't spend their time trying to falsify hypotheses, as Karl Popper wished they did. By the same token, while occasionally particular scientific fields do undergo periods of upheaval, Thomas Kuhn's distinction between "normal science" and scientific "revolutions" is too simple. Was the neo-Darwinian synthesis of the 1930s and 40s in evolutionary biology a revolution or just a significant adjustment? Was Eldredge and Gould's theory of "punctuated equilibria" to explain certain features of the fossil record a blip on the screen or, at least, a minor revolution? But, perhaps, the least convincing feature of the scientific method is not something theorized by philosophers, but something actually practiced by almost every scientist, especially those involved in heavily statistical disciplines such as organismal biology and the social sciences. Whenever we run an experiment, we analyze the data in a way to verify if the so-called "null hypothesis" has been successfully rejected. If so, we open a bottle of champagne and proceed to write up the results to place a new small brick in the edifice of knowledge. Let me explain. A null hypothesis is what would happen if nothing happened. Suppose you are testing the effect of a new drug on the remission of breast cancer. Your null hypothesis is that the drug has no effect: within a properly controlled experimental population, the subjects receiving the drug do not show a statistically significant difference in their remission rate when compared to those who did not receive the drug. If you can reject the null, this is great news: the drug is working, and you have made a potentially important contribution toward bettering humanity's welfare. Or have you? The problem is that the whole idea of a null hypothesis, introduced in statistics by none other than Sir Ronald Fisher (the father of much modern statistical analyses), constraints our questions to 'yes' and 'no' answers. Nature is much too subtle for that. We probably had a pretty good idea, before we even started the experiment, that the null hypothesis was going to be rejected. After all, surely we don't embark in costly (both in terms of material resources and of human potential) experiments just on the whim of the moment. We don't randomly test all possible chemical substances for their role as potential anti-carcinogens. What we really want to know is if the new drug performed better than other, already known, ones