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Title: Knowledge in a Statistical World

Speaker: Conor Mayo-Wilson (University of Washington)

Friday, Nov 21st at 2:30pm in CLE A203

Although there are dozens of philosophical theories that explain our
knowledge of everyday facts, few epistemologists have applied their
theories to scientific knowledge. Can existing theories of knowledge
likewise explain, for example, how we know that the speed of light
is around 186,000 miles per second, that smoking causes cancer, that
poverty and race are correlated in the United States, and so on? In this
paper, I argue that (much of) quantitative empirical science produces
knowledge, in the sense defined by counterfactual theories of
knowledge. The argument has two premises. First, in drawing inferences
from data, scientists often employ classical statistical methods.
Second, those methods guarantee that scientists’ beliefs will satisfy several
modal conditions (e.g., safety and sensitivity) that counterfactual
theories take to be necessary and sufficient conditions for knowledge.