Hypothesis generation

If this were a textbook of statistics then this chapter would not exist. Modern statistics is almost entirely concerned with testing hypotheses, not developing them. This bureaucratic approach views science as a compartmentalised activity in which hypotheses are constructed by one group, data are collected by another group and then the statistician confronts the hypothesis with the data. Since this book is a user's guide to causal modelling such a compartmentalised approach will not do. One of the main challenges faced by the practising biologist is not in testing causal hypotheses but in developing causal hypotheses worth testing.

If this were a book about the philosophy of science then this chapter might not exist either. The philosophy of science mostly deals with questions such as: ‘How can we know whether a scientific hypothesis is true or not?’ or ‘What demarcates a scientific hypothesis from a non-scientific hypothesis?’. For most philosophers of science the question of how one looks for a useful scientific hypothesis in the first place is someone else's problem. For instance, Popper's (1980) influential Logic of scientific discovery says that ‘there is no such thing as a logical method of having new ideas, or a logical reconstruction of this process. My view may be expressed by saying that every discovery contains “an irrational element”, or “a creative intuition” …’ Later, he says that ‘[scientific laws] can only be reached by intuition, based on something like an intellectual love of the objects of experience.’ Again, one gets the impression that science consists to two hermetically sealed compartments.