Learning from Error, Severe Testing, and the Growth of Theoretical Knowledge

The goal of this chapter is to take up the aforementioned challenge as it is posed by Alan Chalmers (1999, 2002), John Earman (1992), Larry Laudan (1997), and other philosophers of science. It may be seen as a first step in taking up some unfinished business noted a decade ago: “How far experimental knowledge can take us in understanding theoretical entities and processes is not something that should be decided before exploring this approach much further” (Mayo, 1996, p. 13). We begin with a sketch of the resources and limitations of the “new experimentalist” philosophy. Learning from evidence, in this experimentalist philosophy, depends not on appraising large-scale theories but on local experimental tasks of estimating backgrounds, modeling data, distinguishing experimental effects, and discriminating signals from noise. The growth of knowledge has not to do with replacing or confirming or probabilifying or “rationally accepting” large-scale theories, but with testing specific hypotheses in such a way that there is a good chance of learning something – whatever theory it winds up as part of. This learning, in the particular experimental account we favor, proceeds by testing experimental hypotheses and inferring those that pass probative or severe tests – tests that would have unearthed some error in, or discrepancy from, a hypothesis H, were H false. What enables this account of severity to work is that the immediate hypothesis H under test by means