Thinking of Biology

608 BioScience • July/August 2007 / Vol. 57 No. 7 www.biosciencemag.org F more than two decades, there has been sustained criticism of the appropriateness of using methods that rely solely on null-hypothesis testing for observational studies in science (e.g., Carver 1978, McBride et al. 1993, Anderson et al. 2000, Wade 2000, Johnson 2002). The disciplines of psychology, wildlife biology, and statistics have been in the forefront of this conflict between two qualitatively different inferential paradigms: model-selection methods, based on information theory, and null-hypothesis testing, based on a frequentist approach. But many other areas of biology and ecology have been implicated, including molecular biology, systematics, physical geography, medicine, and epidemiology (Johnson and Omland 2004). Perhaps this is because all these fields readily provide case studies in which multiple causative factors lead to real-world complexity that is difficult to reduce to a single, isolated mechanism. Strong proponents of the model-selection paradigm have decried the use of null-hypothesis testing as outdated, and some have colorfully suggested that the practice of reporting P values should be “euthanized” on philosophical grounds (Anderson and Burnham 2002). Others have taken an equivocal stance, suggesting that the two inferential paradigms provide complementary tools for the investigator, and that hypothesis testing should be retained for manipulative experimental design (e.g., Johnson and Omland 2004). Stephens and colleagues (2005) proposed that it may be more profitable to distinguish between studies of univariate causality, in which null-hypothesis testing may be sufficient, and multivariate causality, in which model selection offers clear advantages (but see Lukacs et al. 2007). Here we attempt to clarify some of the philosophical terrain relevant to this debate by discussing one of the key philosophical underpinnings of model selection. This is the concept of the method of multiple working hypotheses (MMWH), as described by the geologist T. C. Chamberlin in 1890, and later referred to by Platt (1964) in his notion of “strong inference.” Although the term has become almost mainstream in ecology, we contend that the core meaning of Chamberlin’s conceptualization has often been forgotten or misinterpreted over time, and that this needs rectification. For instance, a common mistake is to equate the MMWH with the method of developing alternative hypotheses. Yet systematic application of the latter method occurred at least as early as Francis Bacon (1620), whereas the former is qualitatively different in construction and was intended by Chamberlin to serve as a complement to the formal, “pure,” or classic analytic method. Here we first describe the MMWH in general terms. Then we discuss its applicability to methodologies that not only allow (or require) the simultaneous appraisal of more than one hypothesis but explicitly accommodate various situations in which several hypotheses are simultaneously true.